transformers gguf llama text-generation-inference unsloth phi sentiment-analysis finetuned nlp en license:apache-2.0 endpoints_compatible region:us conversational
---
base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- phi
- sentiment-analysis
- finetuned
- nlp
---

# Sentiment Finetuned Phi-3

- **Developed by:** Hypernap
- **License:** apache-2.0
- **Finetuned from model :** [unsloth/phi-3.5-mini-instruct-bnb-4bit](https://huggingface.co/unsloth/phi-3.5-mini-instruct-bnb-4bit)

This model is a fine-tuned version of the [unsloth/phi-3.5-mini-instruct-bnb-4bit](https://huggingface.co/unsloth/phi-3.5-mini-instruct-bnb-4bit) using a custom sentiment analysis dataset. It was trained with accelerated speed using [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

## Model Details

This model is a fine-tuned version of the [unsloth/phi-3.5-mini-instruct-bnb-4bit](https://huggingface.co/unsloth/phi-3.5-mini-instruct-bnb-4bit) optimized for sentiment analysis tasks. The original Phi-3 model is a powerful language model, and this fine-tuned version further enhances its capabilities for tasks involving sentiment detection, classification and inference.

**Intended Use:**

This model is intended for use in tasks such as:

*   **Sentiment Analysis:** Classifying the sentiment of text as positive, negative, or neutral.
*   **Customer Feedback Analysis:** Analyzing reviews and feedback for sentiment.
*   **Social Media Monitoring:** Detecting the sentiment of posts and comments.
*   **Text Classification:** General text classification involving sentiment labels.
* **Opinion Mining:** Understanding the sentiment within text data.

**Training Details:**

*   **Fine-tuning Dataset:** A custom sentiment dataset was used for fine-tuning. (Optional: If your dataset is public, you can include a link or a brief description here.)
*   **Training Method:**  The model was fine-tuned using [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face's TRL library, which provides optimized training for models.
*   **Hardware:** The model was trained using [Specify your hardware if you want].
* **Accelerated Training** Using unsloth led to 2x faster training.

**Model Evaluation:**

* (Optional) Provide links to evaluation metrics or example outputs if you have them available. You can include metrics like:
    *   Accuracy
    *   Precision, Recall and F1 scores
    *   Qualitative analysis of the outputs

**Limitations:**

*   The model's performance may vary on datasets significantly different from the training data.
*   It may struggle with sarcasm or nuanced expressions of sentiment.
*   The model is optimized for sentiment analysis tasks, it is not suitable as a generic language model.

**Further Information:**

*   If you have a repository where you keep your training code, datasets, or other relevant information, you can link it here.

**Acknowledgements:**

*   [Unsloth](https://github.com/unslothai/unsloth) for the optimized training library.
*   Hugging Face for the TRL library and model hosting.
*   [Optional] If you have used a specific dataset, give credit to the original creators.

