huggingface

package
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README 

---
title: "Hugging Face"
lang: "en-US"
draft: false
description: "Learn about how to set up a VDP Hugging Face component https://github.com/instill-ai/instill-core"
---

The Hugging Face component is an AI component that allows users to connect the AI models served on the Hugging Face Platform.
It can carry out the following tasks:
- [Text Generation](#text-generation)
- [Fill Mask](#fill-mask)
- [Summarization](#summarization)
- [Text Classification](#text-classification)
- [Token Classification](#token-classification)
- [Translation](#translation)
- [Zero Shot Classification](#zero-shot-classification)
- [Question Answering](#question-answering)
- [Table Question Answering](#table-question-answering)
- [Sentence Similarity](#sentence-similarity)
- [Conversational](#conversational)
- [Image Classification](#image-classification)
- [Image Segmentation](#image-segmentation)
- [Object Detection](#object-detection)
- [Image to Text](#image-to-text)
- [Speech Recognition](#speech-recognition)
- [Audio Classification](#audio-classification)

## Release Stage

`Alpha`

## Configuration

The component definition and tasks are defined in the [definition.json](https://github.com/instill-ai/component/blob/main/ai/huggingface/v0/config/definition.json) and [tasks.json](https://github.com/instill-ai/component/blob/main/ai/huggingface/v0/config/tasks.json) files respectively.

## Setup


In order to communicate with Hugging Face, the following connection details need to be
provided. You may specify them directly in a pipeline recipe as key-value pairs
within the component's `setup` block, or you can create a **Connection** from
the [**Integration Settings**](https://www.instill.tech/docs/vdp/integration)
page and reference the whole `setup` as `setup:
${connection.<my-connection-id>}`.

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| API Key (required) | `api-key` | string | Fill in your Hugging face API token. To find your token, visit <a href="https://huggingface.co/settings/tokens">here</a>  |
| Base URL (required) | `base-url` | string | Hostname for the endpoint. To use Inference API set to <a href="https://api-inference.huggingface.co">here</a>, for Inference Endpoint set to your custom endpoint.  |
| Is Custom Endpoint (required) | `is-custom-endpoint` | boolean | Fill true if you are using a custom Inference Endpoint and not the Inference API.  |

</div>




## Supported Tasks

### Text Generation

Generating text is the task of producing new text. These models can, for example, fill in incomplete text or paraphrase.

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Input | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Task ID (required) | `task` | string | `TASK_TEXT_GENERATION` |
| Model (required) | `model` | string | The Hugging Face model to be used |
| String Input (required) | `inputs` | string | String input |
| [Parameters](#text-generation-parameters) | `parameters` | object | Parameters |
| [Options](#text-generation-options) | `options` | object | Options for the model |
</div>


<details>
<summary> Input Objects in Text Generation</summary>

<h4 id="text-generation-parameters">Parameters</h4>

Parameters

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Do Sample | `do-sample` | boolean | Whether or not to use sampling, use greedy decoding otherwise.  |
| Max New Tokens | `max-new-tokens` | integer | The amount of new tokens to be generated, this does not include the input length it is a estimate of the size of generated text you want. Each new tokens slows down the request, so look for balance between response times and length of text generated.  |
| Max Time | `max-time` | number | The amount of time in seconds that the query should take maximum. Network can cause some overhead so it will be a soft limit. Use that in combination with max-new-tokens for best results.  |
| Num Return Sequences | `num-return-sequences` | integer | The number of proposition you want to be returned.  |
| Repetition Penalty | `repetition-penalty` | number | The more a token is used within generation the more it is penalized to not be picked in successive generation passes.  |
| Return Full Text | `return-full-text` | boolean | If set to False, the return results will not contain the original query making it easier for prompting.  |
| Temperature | `temperature` | number | The temperature of the sampling operation. 1 means regular sampling, 0 means always take the highest score, 100.0 is getting closer to uniform probability.  |
| Top K | `top-k` | integer | Integer to define the top tokens considered within the sample operation to create new text.  |
| Top P | `top-p` | number | Float to define the tokens that are within the sample operation of text generation. Add tokens in the sample for more probable to least probable until the sum of the probabilities is greater than top-p.  |
</div>
<h4 id="text-generation-options">Options</h4>

Options for the model

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Use Cache | `use-cache` | boolean | There is a cache layer on the inference API to speedup requests we have already seen. Most models can use those results as is as models are deterministic (meaning the results will be the same anyway). However if you use a non deterministic model, you can set this parameter to prevent the caching mechanism from being used resulting in a real new query.  |
| Wait For Model | `wait-for-model` | boolean | If the model is not ready, wait for it instead of receiving 503. It limits the number of requests required to get your inference done. It is advised to only set this flag to true after receiving a 503 error as it will limit hanging in your application to known places.  |
</div>
</details>



<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Output | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Generated Text | `generated-text` | string | The continuated string |
</div>

### Fill Mask

Masked language modeling is the task of masking some of the words in a sentence and predicting which words should replace those masks. These models are useful when we want to get a statistical understanding of the language in which the model is trained in.

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Input | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Task ID (required) | `task` | string | `TASK_FILL_MASK` |
| Model (required) | `model` | string | The Hugging Face model to be used |
| String Input (required) | `inputs` | string | a string to be filled from, must contain the [MASK] token (check model card for exact name of the mask) |
| [Options](#fill-mask-options) | `options` | object | Options for the model |
</div>


<details>
<summary> Input Objects in Fill Mask</summary>

<h4 id="fill-mask-options">Options</h4>

Options for the model

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Use Cache | `use-cache` | boolean | There is a cache layer on the inference API to speedup requests we have already seen. Most models can use those results as is as models are deterministic (meaning the results will be the same anyway). However if you use a non deterministic model, you can set this parameter to prevent the caching mechanism from being used resulting in a real new query.  |
| Wait For Model | `wait-for-model` | boolean | If the model is not ready, wait for it instead of receiving 503. It limits the number of requests required to get your inference done. It is advised to only set this flag to true after receiving a 503 error as it will limit hanging in your application to known places.  |
</div>
</details>



<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Output | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| [Results](#fill-mask-results) | `results` | array[object] | Results |
</div>

<details>
<summary> Output Objects in Fill Mask</summary>

<h4 id="fill-mask-results">Results</h4>

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Score | `score` | number | The probability for this token. |
| Sequence | `sequence` | string | The actual sequence of tokens that ran against the model (may contain special tokens) |
| Token | `token` | integer | The id of the token |
| Token Str | `token-str` | string | The string representation of the token |
</div>
</details>

### Summarization

Summarization is the task of producing a shorter version of a document while preserving its important information. Some models can extract text from the original input, while other models can generate entirely new text.

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Input | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Task ID (required) | `task` | string | `TASK_SUMMARIZATION` |
| Model (required) | `model` | string | The Hugging Face model to be used |
| String Input (required) | `inputs` | string | String input |
| [Parameters](#summarization-parameters) | `parameters` | object | Parameters |
| [Options](#summarization-options) | `options` | object | Options for the model |
</div>


<details>
<summary> Input Objects in Summarization</summary>

<h4 id="summarization-parameters">Parameters</h4>

Parameters

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Max Length | `max-length` | integer | Integer to define the maximum length in tokens of the output summary.  |
| Max Time | `max-time` | number | The amount of time in seconds that the query should take maximum. Network can cause some overhead so it will be a soft limit.  |
| Min Length | `min-length` | integer | Integer to define the minimum length in tokens of the output summary.  |
| Repetition Penalty | `repetition-penalty` | number | The more a token is used within generation the more it is penalized to not be picked in successive generation passes.  |
| Temperature | `temperature` | number | The temperature of the sampling operation. 1 means regular sampling, 0 means always take the highest score, 100.0 is getting closer to uniform probability.  |
| Top K | `top-k` | integer | Integer to define the top tokens considered within the sample operation to create new text.  |
| Top P | `top-p` | number | Float to define the tokens that are within the sample operation of text generation. Add tokens in the sample for more probable to least probable until the sum of the probabilities is greater than top-p.  |
</div>
<h4 id="summarization-options">Options</h4>

Options for the model

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Use Cache | `use-cache` | boolean | There is a cache layer on the inference API to speedup requests we have already seen. Most models can use those results as is as models are deterministic (meaning the results will be the same anyway). However if you use a non deterministic model, you can set this parameter to prevent the caching mechanism from being used resulting in a real new query.  |
| Wait For Model | `wait-for-model` | boolean | If the model is not ready, wait for it instead of receiving 503. It limits the number of requests required to get your inference done. It is advised to only set this flag to true after receiving a 503 error as it will limit hanging in your application to known places.  |
</div>
</details>



<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Output | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Summary Text | `summary-text` | string | The string after summarization |
</div>

### Text Classification

Text Classification is the task of assigning a label or class to a given text. Some use cases are sentiment analysis, natural language inference, and assessing grammatical correctness.

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Input | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Task ID (required) | `task` | string | `TASK_TEXT_CLASSIFICATION` |
| Model (required) | `model` | string | The Hugging Face model to be used |
| String Input (required) | `inputs` | string | String input |
| [Options](#text-classification-options) | `options` | object | Options for the model |
</div>


<details>
<summary> Input Objects in Text Classification</summary>

<h4 id="text-classification-options">Options</h4>

Options for the model

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Use Cache | `use-cache` | boolean | There is a cache layer on the inference API to speedup requests we have already seen. Most models can use those results as is as models are deterministic (meaning the results will be the same anyway). However if you use a non deterministic model, you can set this parameter to prevent the caching mechanism from being used resulting in a real new query.  |
| Wait For Model | `wait-for-model` | boolean | If the model is not ready, wait for it instead of receiving 503. It limits the number of requests required to get your inference done. It is advised to only set this flag to true after receiving a 503 error as it will limit hanging in your application to known places.  |
</div>
</details>



<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Output | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| [Results](#text-classification-results) | `results` | array[object] | Results |
</div>

<details>
<summary> Output Objects in Text Classification</summary>

<h4 id="text-classification-results">Results</h4>

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Label | `label` | string | The label for the class (model specific) |
| Score | `score` | number | A floats that represents how likely is that the text belongs the this class. |
</div>
</details>

### Token Classification

Token classification is a natural language understanding task in which a label is assigned to some tokens in a text. Some popular token classification subtasks are Named Entity Recognition (NER) and Part-of-Speech (PoS) tagging. NER models could be trained to identify specific entities in a text, such as dates, individuals and places; and PoS tagging would identify, for example, which words in a text are verbs, nouns, and punctuation marks.

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Input | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Task ID (required) | `task` | string | `TASK_TOKEN_CLASSIFICATION` |
| Model (required) | `model` | string | The Hugging Face model to be used |
| String Input (required) | `inputs` | string | String input |
| [Parameters](#token-classification-parameters) | `parameters` | object | Parameters |
| [Options](#token-classification-options) | `options` | object | Options for the model |
</div>


<details>
<summary> Input Objects in Token Classification</summary>

<h4 id="token-classification-parameters">Parameters</h4>

Parameters

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Aggregation Strategy | `aggregation-strategy` | string | There are several aggregation strategies:
none: Every token gets classified without further aggregation.
simple: Entities are grouped according to the default schema (B-, I- tags get merged when the tag is similar).
first: Same as the simple strategy except words cannot end up with different tags. Words will use the tag of the first token when there is ambiguity.
average: Same as the simple strategy except words cannot end up with different tags. Scores are averaged across tokens and then the maximum label is applied.
max: Same as the simple strategy except words cannot end up with different tags. Word entity will be the token with the maximum score.  |
</div>
<h4 id="token-classification-options">Options</h4>

Options for the model

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Use Cache | `use-cache` | boolean | There is a cache layer on the inference API to speedup requests we have already seen. Most models can use those results as is as models are deterministic (meaning the results will be the same anyway). However if you use a non deterministic model, you can set this parameter to prevent the caching mechanism from being used resulting in a real new query.  |
| Wait For Model | `wait-for-model` | boolean | If the model is not ready, wait for it instead of receiving 503. It limits the number of requests required to get your inference done. It is advised to only set this flag to true after receiving a 503 error as it will limit hanging in your application to known places.  |
</div>
</details>



<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Output | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| [Results](#token-classification-results) | `results` | array[object] | Results |
</div>

<details>
<summary> Output Objects in Token Classification</summary>

<h4 id="token-classification-results">Results</h4>

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| End | `end` | integer | The offset stringwise where the answer is located. Useful to disambiguate if word occurs multiple times. |
| Entity Group | `entity-group` | string | The type for the entity being recognized (model specific). |
| Score | `score` | number | How likely the entity was recognized. |
| Start | `start` | integer | The offset stringwise where the answer is located. Useful to disambiguate if word occurs multiple times. |
| Word | `word` | string | The string that was captured |
</div>
</details>

### Translation

Translation is the task of converting text from one language to another.

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Input | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Task ID (required) | `task` | string | `TASK_TRANSLATION` |
| Model (required) | `model` | string | The Hugging Face model to be used |
| String Input (required) | `inputs` | string | String input |
| [Options](#translation-options) | `options` | object | Options for the model |
</div>


<details>
<summary> Input Objects in Translation</summary>

<h4 id="translation-options">Options</h4>

Options for the model

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Use Cache | `use-cache` | boolean | There is a cache layer on the inference API to speedup requests we have already seen. Most models can use those results as is as models are deterministic (meaning the results will be the same anyway). However if you use a non deterministic model, you can set this parameter to prevent the caching mechanism from being used resulting in a real new query.  |
| Wait For Model | `wait-for-model` | boolean | If the model is not ready, wait for it instead of receiving 503. It limits the number of requests required to get your inference done. It is advised to only set this flag to true after receiving a 503 error as it will limit hanging in your application to known places.  |
</div>
</details>



<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Output | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Translation Text | `translation-text` | string | The string after translation |
</div>

### Zero Shot Classification

Zero-shot text classification is a task in natural language processing where a model is trained on a set of labeled examples but is then able to classify new examples from previously unseen classes.

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Input | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Task ID (required) | `task` | string | `TASK_ZERO_SHOT_CLASSIFICATION` |
| Model (required) | `model` | string | The Hugging Face model to be used |
| String Input (required) | `inputs` | string | String input |
| [Parameters](#zero-shot-classification-parameters) | `parameters` | object | Parameters |
| [Options](#zero-shot-classification-options) | `options` | object | Options for the model |
</div>


<details>
<summary> Input Objects in Zero Shot Classification</summary>

<h4 id="zero-shot-classification-parameters">Parameters</h4>

Parameters

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Candidate Labels | `candidate-labels` | array | a list of strings that are potential classes for inputs. (max 10 candidate-labels, for more, simply run multiple requests, results are going to be misleading if using too many candidate-labels anyway. If you want to keep the exact same, you can simply run multi-label=True and do the scaling on your end. )  |
| Multi Label | `multi-label` | boolean | Boolean that is set to True if classes can overlap  |
</div>
<h4 id="zero-shot-classification-options">Options</h4>

Options for the model

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Use Cache | `use-cache` | boolean | There is a cache layer on the inference API to speedup requests we have already seen. Most models can use those results as is as models are deterministic (meaning the results will be the same anyway). However if you use a non deterministic model, you can set this parameter to prevent the caching mechanism from being used resulting in a real new query.  |
| Wait For Model | `wait-for-model` | boolean | If the model is not ready, wait for it instead of receiving 503. It limits the number of requests required to get your inference done. It is advised to only set this flag to true after receiving a 503 error as it will limit hanging in your application to known places.  |
</div>
</details>



<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Output | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Scores | `scores` | array[number] | a list of floats that correspond the the probability of label, in the same order as labels. |
| Labels | `labels` | array[string] | The list of strings for labels that you sent (in order) |
| Sequence (optional) | `sequence` | string | The string sent as an input |
</div>

### Question Answering

Question Answering models can retrieve the answer to a question from a given text, which is useful for searching for an answer in a document. Some question answering models can generate answers without context!

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Input | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Task ID (required) | `task` | string | `TASK_QUESTION_ANSWERING` |
| Model (required) | `model` | string | The Hugging Face model to be used |
| [Inputs](#question-answering-inputs) (required) | `inputs` | object | Inputs |
| [Options](#question-answering-options) | `options` | object | Options for the model |
</div>


<details>
<summary> Input Objects in Question Answering</summary>

<h4 id="question-answering-inputs">Inputs</h4>

Inputs

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Context | `context` | string | The context for answering the question.  |
| Question | `question` | string | The question  |
</div>
<h4 id="question-answering-options">Options</h4>

Options for the model

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Use Cache | `use-cache` | boolean | There is a cache layer on the inference API to speedup requests we have already seen. Most models can use those results as is as models are deterministic (meaning the results will be the same anyway). However if you use a non deterministic model, you can set this parameter to prevent the caching mechanism from being used resulting in a real new query.  |
| Wait For Model | `wait-for-model` | boolean | If the model is not ready, wait for it instead of receiving 503. It limits the number of requests required to get your inference done. It is advised to only set this flag to true after receiving a 503 error as it will limit hanging in your application to known places.  |
</div>
</details>



<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Output | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Answer | `answer` | string | A string that’s the answer within the text. |
| Stop (optional) | `stop` | integer | The index (string wise) of the stop of the answer within context. |
| Score (optional) | `score` | number | A float that represents how likely that the answer is correct |
| Start (optional) | `start` | integer | The index (string wise) of the start of the answer within context. |
</div>

### Table Question Answering

Table Question Answering (Table QA) is the answering a question about an information on a given table.

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Input | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Task ID (required) | `task` | string | `TASK_TABLE_QUESTION_ANSWERING` |
| Model (required) | `model` | string | The Hugging Face model to be used |
| [Inputs](#table-question-answering-inputs) (required) | `inputs` | object | Inputs |
| [Options](#table-question-answering-options) | `options` | object | Options for the model |
</div>


<details>
<summary> Input Objects in Table Question Answering</summary>

<h4 id="table-question-answering-inputs">Inputs</h4>

Inputs

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Query | `query` | string | The query in plain text that you want to ask the table  |
| Table | `table` | object | A table of data represented as a dict of list where entries are headers and the lists are all the values, all lists must have the same size.  |
</div>
<h4 id="table-question-answering-options">Options</h4>

Options for the model

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Use Cache | `use-cache` | boolean | There is a cache layer on the inference API to speedup requests we have already seen. Most models can use those results as is as models are deterministic (meaning the results will be the same anyway). However if you use a non deterministic model, you can set this parameter to prevent the caching mechanism from being used resulting in a real new query.  |
| Wait For Model | `wait-for-model` | boolean | If the model is not ready, wait for it instead of receiving 503. It limits the number of requests required to get your inference done. It is advised to only set this flag to true after receiving a 503 error as it will limit hanging in your application to known places.  |
</div>
</details>



<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Output | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Aggregator (optional) | `aggregator` | string | The aggregator used to get the answer |
| Answer | `answer` | string | The plaintext answer |
| Cells (optional) | `cells` | array[string] | a list of coordinates of the cells contents |
| Coordinates (optional) | `coordinates` | array[array] | a list of coordinates of the cells referenced in the answer |
</div>

### Sentence Similarity

Sentence Similarity is the task of determining how similar two texts are. Sentence similarity models convert input texts into vectors (embeddings) that capture semantic information and calculate how close (similar) they are between them. This task is particularly useful for information retrieval and clustering/grouping.

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Input | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Task ID (required) | `task` | string | `TASK_SENTENCE_SIMILARITY` |
| Model (required) | `model` | string | The Hugging Face model to be used |
| [Inputs](#sentence-similarity-inputs) (required) | `inputs` | object | Inputs |
| [Options](#sentence-similarity-options) | `options` | object | Options for the model |
</div>


<details>
<summary> Input Objects in Sentence Similarity</summary>

<h4 id="sentence-similarity-inputs">Inputs</h4>

Inputs

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Sentences | `sentences` | array | A list of strings which will be compared against the source-sentence.  |
| Source Sentence | `source-sentence` | string | The string that you wish to compare the other strings with. This can be a phrase, sentence, or longer passage, depending on the model being used.  |
</div>
<h4 id="sentence-similarity-options">Options</h4>

Options for the model

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Use Cache | `use-cache` | boolean | There is a cache layer on the inference API to speedup requests we have already seen. Most models can use those results as is as models are deterministic (meaning the results will be the same anyway). However if you use a non deterministic model, you can set this parameter to prevent the caching mechanism from being used resulting in a real new query.  |
| Wait For Model | `wait-for-model` | boolean | If the model is not ready, wait for it instead of receiving 503. It limits the number of requests required to get your inference done. It is advised to only set this flag to true after receiving a 503 error as it will limit hanging in your application to known places.  |
</div>
</details>



<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Output | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Scores | `scores` | array[number] | The associated similarity score for each of the given strings |
</div>

### Conversational

Conversational response modelling is the task of generating conversational text that is relevant, coherent and knowledgable given a prompt. These models have applications in chatbots, and as a part of voice assistants

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Input | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Task ID (required) | `task` | string | `TASK_CONVERSATIONAL` |
| Model (required) | `model` | string | The Hugging Face model to be used |
| [Inputs](#conversational-inputs) (required) | `inputs` | object | Inputs |
| [Parameters](#conversational-parameters) | `parameters` | object | Parameters |
| [Options](#conversational-options) | `options` | object | Options for the model |
</div>


<details>
<summary> Input Objects in Conversational</summary>

<h4 id="conversational-inputs">Inputs</h4>

Inputs

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Generated Responses | `generated-responses` | array | A list of strings corresponding to the earlier replies from the model.  |
| Past User Inputs | `past-user-inputs` | array | A list of strings corresponding to the earlier replies from the user. Should be of the same length of generated-responses.  |
| Text | `text` | string | The last input from the user in the conversation.  |
</div>
<h4 id="conversational-parameters">Parameters</h4>

Parameters

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Max Length | `max-length` | integer | Integer to define the maximum length in tokens of the output summary.  |
| Max Time | `max-time` | number | The amount of time in seconds that the query should take maximum. Network can cause some overhead so it will be a soft limit.  |
| Min Length | `min-length` | integer | Integer to define the minimum length in tokens of the output summary.  |
| Repetition Penalty | `repetition-penalty` | number | The more a token is used within generation the more it is penalized to not be picked in successive generation passes.  |
| Temperature | `temperature` | number | The temperature of the sampling operation. 1 means regular sampling, 0 means always take the highest score, 100.0 is getting closer to uniform probability.  |
| Top K | `top-k` | integer | Integer to define the top tokens considered within the sample operation to create new text.  |
| Top P | `top-p` | number | Float to define the tokens that are within the sample operation of text generation. Add tokens in the sample for more probable to least probable until the sum of the probabilities is greater than top-p.  |
</div>
<h4 id="conversational-options">Options</h4>

Options for the model

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Use Cache | `use-cache` | boolean | There is a cache layer on the inference API to speedup requests we have already seen. Most models can use those results as is as models are deterministic (meaning the results will be the same anyway). However if you use a non deterministic model, you can set this parameter to prevent the caching mechanism from being used resulting in a real new query.  |
| Wait For Model | `wait-for-model` | boolean | If the model is not ready, wait for it instead of receiving 503. It limits the number of requests required to get your inference done. It is advised to only set this flag to true after receiving a 503 error as it will limit hanging in your application to known places.  |
</div>
</details>



<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Output | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| [Conversation](#conversational-conversation) (optional) | `conversation` | object | A facility dictionary to send back for the next input (with the new user input addition). |
| Generated Text | `generated-text` | string | The answer of the bot |
</div>

<details>
<summary> Output Objects in Conversational</summary>

<h4 id="conversational-conversation">Conversation</h4>

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Generated Responses | `generated-responses` | array | List of strings. The last outputs from the model in the conversation, after the model has run. |
| Past User Inputs | `past-user-inputs` | array | List of strings. The last inputs from the user in the conversation, after the model has run. |
</div>
</details>

### Image Classification

Image classification is the task of assigning a label or class to an entire image. Images are expected to have only one class for each image. Image classification models take an image as input and return a prediction about which class the image belongs to.

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Input | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Task ID (required) | `task` | string | `TASK_IMAGE_CLASSIFICATION` |
| Model (required) | `model` | string | The Hugging Face model to be used |
| Image (required) | `image` | string | The image file |
</div>






<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Output | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| [Classes](#image-classification-classes) | `classes` | array[object] | Classes |
</div>

<details>
<summary> Output Objects in Image Classification</summary>

<h4 id="image-classification-classes">Classes</h4>

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Label | `label` | string | The label for the class (model specific) |
| Score | `score` | number | A float that represents how likely it is that the image file belongs to this class. |
</div>
</details>

### Image Segmentation

Image Segmentation divides an image into segments where each pixel in the image is mapped to an object. This task has multiple variants such as instance segmentation, panoptic segmentation and semantic segmentation.

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Input | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Task ID (required) | `task` | string | `TASK_IMAGE_SEGMENTATION` |
| Model (required) | `model` | string | The Hugging Face model to be used |
| Image (required) | `image` | string | The image file |
</div>






<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Output | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| [Segments](#image-segmentation-segments) | `segments` | array[object] | Segments |
</div>

<details>
<summary> Output Objects in Image Segmentation</summary>

<h4 id="image-segmentation-segments">Segments</h4>

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Label | `label` | string | The label for the class (model specific) of a segment. |
| Mask | `mask` | string | A str (base64 str of a single channel black-and-white img) representing the mask of a segment. |
| Score | `score` | number | A float that represents how likely it is that the segment belongs to the given class. |
</div>
</details>

### Object Detection

Object Detection models allow users to identify objects of certain defined classes. Object detection models receive an image as input and output the images with bounding boxes and labels on detected objects.

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Input | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Task ID (required) | `task` | string | `TASK_OBJECT_DETECTION` |
| Model (required) | `model` | string | The Hugging Face model to be used |
| Image (required) | `image` | string | The image file |
</div>






<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Output | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| [Objects](#object-detection-objects) | `objects` | array[object] | Objects |
</div>

<details>
<summary> Output Objects in Object Detection</summary>

<h4 id="object-detection-objects">Objects</h4>

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| [Box](#object-detection-box) | `box` | object | A dict (with keys [xmin,ymin,xmax,ymax]) representing the bounding box of a detected object. |
| Label | `label` | string | The label for the class (model specific) of a detected object. |
| Score | `score` | number | A float that represents how likely it is that the detected object belongs to the given class. |
</div>

<h4 id="object-detection-box">Box</h4>

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| X Max | `xmax` | number | X max |
| X Min | `xmin` | number | X min |
| Y Max | `ymax` | number | Y Max |
| Y min | `ymin` | number | Y min |
</div>
</details>

### Image to Text

Image to text models output a text from a given image. Image captioning or optical character recognition can be considered as the most common applications of image to text.

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Input | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Task ID (required) | `task` | string | `TASK_IMAGE_TO_TEXT` |
| Model (required) | `model` | string | The Hugging Face model to be used |
| Image (required) | `image` | string | The image file |
</div>






<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Output | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Text | `text` | string | Generated text |
</div>

### Speech Recognition

Automatic Speech Recognition (ASR), also known as Speech to Text (STT), is the task of transcribing a given audio to text. It has many applications, such as voice user interfaces.

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Input | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Task ID (required) | `task` | string | `TASK_SPEECH_RECOGNITION` |
| Model (required) | `model` | string | The Hugging Face model to be used |
| Audio (required) | `audio` | string | The audio file |
</div>






<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Output | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Text | `text` | string | The string that was recognized within the audio file. |
</div>

### Audio Classification

Audio classification is the task of assigning a label or class to a given audio. It can be used for recognizing which command a user is giving or the emotion of a statement, as well as identifying a speaker.

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Input | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| Task ID (required) | `task` | string | `TASK_AUDIO_CLASSIFICATION` |
| Model (required) | `model` | string | The Hugging Face model to be used |
| Audio (required) | `audio` | string | The audio file |
</div>






<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Output | ID | Type | Description |
| :--- | :--- | :--- | :--- |
| [Classes](#audio-classification-classes) | `classes` | array[object] | Classes |
</div>

<details>
<summary> Output Objects in Audio Classification</summary>

<h4 id="audio-classification-classes">Classes</h4>

<div class="markdown-col-no-wrap" data-col-1 data-col-2>

| Field | Field ID | Type | Note |
| :--- | :--- | :--- | :--- |
| Label | `label` | string | The label for the class (model specific) |
| Score | `score` | number | A float that represents how likely it is that the audio file belongs to this class. |
</div>
</details>

Documentation

Index

Constants

This section is empty.

Variables

This section is empty.

Functions

func Init 

func Init(bc base.Component) *component

Types

type AudioRequest 

type AudioRequest struct {
	Audio string `json:"audio"`
}

type ConversationalInputs 

type ConversationalInputs struct {
	// (Required) The last input from the user in the conversation.
	Text string `json:"text"`

	// A list of strings corresponding to the earlier replies from the model.
	GeneratedResponses []string `json:"generated_responses,omitempty" instill:"generated-responses"`

	// A list of strings corresponding to the earlier replies from the user.
	// Should be of the same length of GeneratedResponses.
	PastUserInputs []string `json:"past_user_inputs,omitempty" instill:"past-user-inputs"`
}

Used with ConversationalRequest

type ConversationalParameters 

type ConversationalParameters struct {
	// (Default: None). Integer to define the minimum length in tokens of the output summary.
	MinLength *int `json:"min_length,omitempty" instill:"min-length"`

	// (Default: None). Integer to define the maximum length in tokens of the output summary.
	MaxLength *int `json:"max_length,omitempty" instill:"max-length"`

	// (Default: None). Integer to define the top tokens considered within the sample operation to create
	// new text.
	TopK *int `json:"top_k,omitempty" instill:"top-k"`

	// (Default: None). Float to define the tokens that are within the sample` operation of text generation.
	// Add tokens in the sample for more probable to least probable until the sum of the probabilities is
	// greater than top_p.
	TopP *float64 `json:"top_p,omitempty" instill:"top-p"`

	// (Default: 1.0). Float (0.0-100.0). The temperature of the sampling operation. 1 means regular sampling,
	// 0 mens top_k=1, 100.0 is getting closer to uniform probability.
	Temperature *float64 `json:"temperature,omitempty"`

	// (Default: None). Float (0.0-100.0). The more a token is used within generation the more it is penalized
	// to not be picked in successive generation passes.
	RepetitionPenalty *float64 `json:"repetition_penalty,omitempty" instill:"repetition-penalty"`

	// (Default: None). Float (0-120.0). The amount of time in seconds that the query should take maximum.
	// Network can cause some overhead so it will be a soft limit.
	MaxTime *float64 `json:"maxtime,omitempty"`
}

Used with ConversationalRequest

type ConversationalRequest 

type ConversationalRequest struct {
	// (Required)
	Inputs ConversationalInputs `json:"inputs"`

	Parameters ConversationalParameters `json:"parameters,omitempty"`
	Options    Options                  `json:"options,omitempty"`
}

Request structure for the conversational endpoint

type ConversationalResponse 

type ConversationalResponse struct {
	GgeneratedText string `json:"generated_text" instill:"generated-text"`
}

type FeatureExtractionRequest 

type FeatureExtractionRequest struct {
	// (Required)
	Inputs string `json:"inputs"`

	Options Options `json:"options,omitempty"`
}

type FillMaskRequest 

type FillMaskRequest struct {
	// (Required) a string to be filled from, must contain the [MASK] token (check model card for exact name of the mask)
	Inputs  string  `json:"inputs,omitempty"`
	Options Options `json:"options,omitempty"`
}

Request structure for the Fill Mask endpoint

type ImageRequest 

type ImageRequest struct {
	Image string `json:"image"`
}

type ImageSegmentationResponse 

type ImageSegmentationResponse struct {
	// The label for the class (model specific) of a segment.
	Label string `json:"label,omitempty"`

	// A float that represents how likely it is that the segment belongs to the given class.
	Score float64 `json:"score,omitempty"`

	// A str (base64 str of a single channel black-and-white img) representing the mask of a segment.
	Mask string `json:"mask,omitempty"`
}

type ImageToTextResponse 

type ImageToTextResponse struct {
	// The generated caption
	GeneratedText string `json:"generated_text" instill:"generated-text"`
}

type ObjectBox 

type ObjectBox struct {
	XMin int `json:"xmin,omitempty"`
	YMin int `json:"ymin,omitempty"`
	XMax int `json:"xmax,omitempty"`
	YMax int `json:"ymax,omitempty"`
}

type Options 

type Options struct {
	// (Default: false). Boolean to use GPU instead of CPU for inference.
	// Requires Startup plan at least.
	UseGPU *bool `json:"use_gpu,omitempty" instill:"use-gpu"`
	// (Default: true). There is a cache layer on the inference API to speedup
	// requests we have already seen. Most models can use those results as is
	// as models are deterministic (meaning the results will be the same anyway).
	// However if you use a non deterministic model, you can set this parameter
	// to prevent the caching mechanism from being used resulting in a real new query.
	UseCache *bool `json:"use_cache,omitempty" instill:"use-cache"`
	// (Default: false) If the model is not ready, wait for it instead of receiving 503.
	// It limits the number of requests required to get your inference done. It is advised
	// to only set this flag to true after receiving a 503 error as it will limit hanging
	// in your application to known places.
	WaitForModel *bool `json:"wait_for_model,omitempty" instill:"wait-for-model"`
}

type QuestionAnsweringInputs 

type QuestionAnsweringInputs struct {
	// (Required) The question as a string that has an answer within Context.
	Question string `json:"question"`

	// (Required) A string that contains the answer to the question
	Context string `json:"context"`
}

type QuestionAnsweringRequest 

type QuestionAnsweringRequest struct {
	// (Required)
	Inputs  QuestionAnsweringInputs `json:"inputs"`
	Options Options                 `json:"options,omitempty"`
}

Request structure for question answering model

type QuestionAnsweringResponse 

type QuestionAnsweringResponse struct {
	// A string that’s the answer within the Context text.
	Answer string `json:"answer,omitempty"`

	// A float that represents how likely that the answer is correct.
	Score float64 `json:"score,omitempty"`

	// The string index of the start of the answer within Context.
	Start int `json:"start,omitempty"`

	// The string index of the stop of the answer within Context.
	Stop int `json:"stop,omitempty"`
}

Response structure for question answering model

type SentenceSimilarityInputs 

type SentenceSimilarityInputs struct {
	// (Required) The string that you wish to compare the other strings with.
	// This can be a phrase, sentence, or longer passage, depending on the
	// model being used.
	SourceSentence string `json:"source_sentence" instill:"source-sentence"`

	// A list of strings which will be compared against the source_sentence.
	Sentences []string `json:"sentences"`
}

type SentenceSimilarityRequest 

type SentenceSimilarityRequest struct {
	// (Required) Inputs for the request.
	Inputs  SentenceSimilarityInputs `json:"inputs"`
	Options Options                  `json:"options,omitempty"`
}

Request structure for the Sentence Similarity endpoint.

type SpeechRecognitionResponse 

type SpeechRecognitionResponse struct {
	// The string that was recognized within the audio file.
	Text string `json:"text,omitempty"`
}

type SummarizationParameters 

type SummarizationParameters struct {
	// (Default: None). Integer to define the minimum length in tokens of the output summary.
	MinLength *int `json:"min_length,omitempty" instill:"min-length"`

	// (Default: None). Integer to define the maximum length in tokens of the output summary.
	MaxLength *int `json:"max_length,omitempty" instill:"max-length"`

	// (Default: None). Integer to define the top tokens considered within the sample operation to create
	// new text.
	TopK *int `json:"top_k,omitempty" instill:"top-k"`

	// (Default: None). Float to define the tokens that are within the sample` operation of text generation.
	// Add tokens in the sample for more probable to least probable until the sum of the probabilities is
	// greater than top_p.
	TopP *float64 `json:"top_p,omitempty" instill:"top-p"`

	// (Default: 1.0). Float (0.0-100.0). The temperature of the sampling operation. 1 means regular sampling,
	// 0 mens top_k=1, 100.0 is getting closer to uniform probability.
	Temperature *float64 `json:"temperature,omitempty"`

	// (Default: None). Float (0.0-100.0). The more a token is used within generation the more it is penalized
	// to not be picked in successive generation passes.
	RepetitionPenalty *float64 `json:"repetitionpenalty,omitempty"`

	// (Default: None). Float (0-120.0). The amount of time in seconds that the query should take maximum.
	// Network can cause some overhead so it will be a soft limit.
	MaxTime *float64 `json:"maxtime,omitempty"`
}

Used with SummarizationRequest

type SummarizationRequest 

type SummarizationRequest struct {
	// String to be summarized
	Inputs     string                  `json:"inputs"`
	Parameters SummarizationParameters `json:"parameters,omitempty"`
	Options    Options                 `json:"options,omitempty"`
}

Request structure for the summarization endpoint

type SummarizationResponse 

type SummarizationResponse struct {
	// The summarized input string
	SummaryText string `json:"summary_text,omitempty" instill:"summary-text"`
}

Response structure for the summarization endpoint

type TableQuestionAnsweringInputs 

type TableQuestionAnsweringInputs struct {
	// (Required) The query in plain text that you want to ask the table
	Query string `json:"query"`

	// (Required) A table of data represented as a dict of list where entries
	// are headers and the lists are all the values, all lists must
	// have the same size.
	Table map[string][]string `json:"table"`
}

type TableQuestionAnsweringRequest 

type TableQuestionAnsweringRequest struct {
	Inputs  TableQuestionAnsweringInputs `json:"inputs"`
	Options Options                      `json:"options,omitempty"`
}

Request structure for table question answering model

type TableQuestionAnsweringResponse 

type TableQuestionAnsweringResponse struct {
	// The plaintext answer
	Answer string `json:"answer,omitempty"`

	// A list of coordinates of the cells references in the answer
	Coordinates [][]int `json:"coordinates,omitempty"`

	// A list of coordinates of the cells contents
	Cells []string `json:"cells,omitempty"`

	// The aggregator used to get the answer
	Aggregator string `json:"aggregator,omitempty"`
}

Response structure for table question answering model

type TextClassificationRequest 

type TextClassificationRequest struct {
	//String to be classified
	Inputs  string  `json:"inputs"`
	Options Options `json:"options,omitempty"`
}

Request structure for the Text classification endpoint

type TextGenerationParameters 

type TextGenerationParameters struct {
	// (Default: None). Integer to define the top tokens considered within the sample operation to create new text.
	TopK *int `json:"top_k,omitempty" instill:"top-k"`

	// (Default: None). Float to define the tokens that are within the sample` operation of text generation. Add
	// tokens in the sample for more probable to least probable until the sum of the probabilities is greater
	// than top_p.
	TopP *float64 `json:"top_p,omitempty" instill:"top-p"`

	// (Default: 1.0). Float (0.0-100.0). The temperature of the sampling operation. 1 means regular sampling,
	// 0 means top_k=1, 100.0 is getting closer to uniform probability.
	Temperature *float64 `json:"temperature,omitempty"`

	// (Default: None). Float (0.0-100.0). The more a token is used within generation the more it is penalized
	// to not be picked in successive generation passes.
	RepetitionPenalty *float64 `json:"repetition_penalty,omitempty" instill:"repetition-penalty"`

	// (Default: None). Int (0-250). The amount of new tokens to be generated, this does not include the input
	// length it is a estimate of the size of generated text you want. Each new tokens slows down the request,
	// so look for balance between response times and length of text generated.
	MaxNewTokens *int `json:"max_new_tokens,omitempty" instill:"max-new-tokens"`

	// (Default: None). Float (0-120.0). The amount of time in seconds that the query should take maximum.
	// Network can cause some overhead so it will be a soft limit. Use that in combination with max_new_tokens
	// for best results.
	MaxTime *float64 `json:"max_time,omitempty" instill:"max-time"`

	// (Default: True). Bool. If set to False, the return results will not contain the original query making it
	// easier for prompting.
	ReturnFullText *bool `json:"return_full_text,omitempty" instill:"return-full-text"`

	// (Default: 1). Integer. The number of proposition you want to be returned.
	NumReturnSequences *int `json:"num_return_sequences,omitempty" instill:"num-return-sequences"`
}

type TextGenerationRequest 

type TextGenerationRequest struct {
	// (Required) a string to be generated from
	Inputs     string                   `json:"inputs"`
	Parameters TextGenerationParameters `json:"parameters,omitempty"`
	Options    Options                  `json:"options,omitempty"`
}

type TextGenerationResponse 

type TextGenerationResponse struct {
	GeneratedText string `json:"generated_text,omitempty" instill:"generated-text"`
}

type TextToImageRequest 

type TextToImageRequest struct {
	// The prompt or prompts to guide the image generation.
	Inputs     string                       `json:"inputs"`
	Options    Options                      `json:"options,omitempty"`
	Parameters TextToImageRequestParameters `json:"parameters,omitempty"`
}

Request structure for text-to-image model

type TextToImageRequestParameters 

type TextToImageRequestParameters struct {
	// The prompt or prompts not to guide the image generation.
	// Ignored when not using guidance (i.e., ignored if guidance_scale is less than 1).
	NegativePrompt string `json:"negative_prompt,omitempty" instill:"negative-prompt"`
	// The height in pixels of the generated image.
	Height int64 `json:"height,omitempty"`
	// The width in pixels of the generated image.
	Width int64 `json:"width,omitempty"`
	// The number of denoising steps. More denoising steps usually lead to a higher quality
	// image at the expense of slower inference. Defaults to 50.
	NumInferenceSteps int64 `json:"num_inference_steps,omitempty" instill:"num-inference-steps"`
	// Higher guidance scale encourages to generate images that are closely linked to the text
	// input, usually at the expense of lower image quality. Defaults to 7.5.
	GuidanceScale float64 `json:"guidance_scale,omitempty" instill:"guidance-scale"`
}

type TokenClassificationParameters 

type TokenClassificationParameters struct {
	// (Default: simple)
	AggregationStrategy string `json:"aggregation_strategy,omitempty" instill:"aggregation-strategy"`
}

type TokenClassificationRequest 

type TokenClassificationRequest struct {
	// (Required) strings to be classified
	Inputs     string                        `json:"inputs"`
	Parameters TokenClassificationParameters `json:"parameters,omitempty"`
	Options    Options                       `json:"options,omitempty"`
}

Request structure for the token classification endpoint

type TranslationRequest 

type TranslationRequest struct {
	// (Required) a string to be translated in the original languages
	Inputs string `json:"inputs"`

	Options Options `json:"options,omitempty"`
}

Request structure for the Translation endpoint

type TranslationResponse 

type TranslationResponse struct {
	// The translated Input string
	TranslationText string `json:"translation_text,omitempty" instill:"translation-text"`
}

Response structure from the Translation endpoint

type ZeroShotParameters 

type ZeroShotParameters struct {
	// (Required) A list of strings that are potential classes for inputs. Max 10 candidate_labels,
	// for more, simply run multiple requests, results are going to be misleading if using
	// too many candidate_labels anyway. If you want to keep the exact same, you can
	// simply run multi_label=True and do the scaling on your end.
	CandidateLabels []string `json:"candidate_labels" instill:"candidate-labels"`

	// (Default: false) Boolean that is set to True if classes can overlap
	MultiLabel *bool `json:"multi_label,omitempty" instill:"multi-label"`
}

Used with ZeroShotRequest

type ZeroShotRequest 

type ZeroShotRequest struct {
	// (Required)
	Inputs string `json:"inputs"`

	// (Required)
	Parameters ZeroShotParameters `json:"parameters"`

	Options Options `json:"options,omitempty"`
}

type ZeroShotResponse 

type ZeroShotResponse struct {
	// The string sent as an input
	Sequence string `json:"sequence,omitempty"`

	// The list of labels sent in the request, sorted in descending order
	// by probability that the input corresponds to the to the label.
	Labels []string `json:"labels,omitempty"`

	// a list of floats that correspond the the probability of label, in the same order as labels.
	Scores []float64 `json:"scores,omitempty"`
}

Response structure from the Zero-shot classification endpoint.

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