# Sentiment Analysis

**Supported Labeling Types**: `Row labeling`

Sentiment analysis will provide a simple classification between “Positive” or “Negative”.

The model used for sentiment analysis is sourced from [DistilBERT-base-uncased-finetuned-SST-2](https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english). This model is a fine-tuned checkpoint of DistilBERT-base-uncased, specifically trained on the SST-2 dataset.

<figure><img src="https://448889121-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-MbjY0HseEqu7LtYAt4d%2Fuploads%2Fgit-blob-8d47614ef03144385abdc3d1b2e47f6bea7bc1f1%2FExtension%20-%20ML-assisted%20Labeling%20-%20Row%20labeling%20-%20Sentiment%20analysis%20-%20highlight.png?alt=media" alt="Image of ML Assisted with Sentiment Analysis"><figcaption><p>ML Assisted with Sentiment Analysis</p></figcaption></figure>

### Model Details

* The model is using a distilled version developed by Hugging Face based on the Text Classification task pipeline.
* Trained on Stanford Sentiment Treebank ([sst2](https://huggingface.co/datasets/stanfordnlp/sst2)) corpora which contains 67,349 movie review excerpts with human-annotated sentiment labels.
* The model reaches an accuracy of **91.3%** on the development set.
* The model hosted locally within the Datasaur Intelligence container.

### Usage

* This model is primarily used for sentiment classification and also be used for topic classification.
* The raw model supports masked language modeling and next sentence prediction, though it is primarily intended for fine-tuning on downstream tasks.
* To explore additional fine-tuned versions for different tasks, check out the [Hugging Face model hub](https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english).


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.datasaur.ai/assisted-labeling/ml-assisted-labeling/sentiment-analysis.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.