---
library_name: transformers
license: mit
language:
- en
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-classification
---

# Model Card for Model ID

Texts text input and classifies the text into 8 classes of emotions- neutral, anger, love, fear, hate, happiness, sadness, surprise

## Model Details

### Model Description

- **Developed by:** Antareep, Eswar, Subhasish
- **Model type:** Large Language Model(LLM)
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model:** BERT-Base

### Model Sources [optional]

- **Repository:** https://huggingface.co/google-bert/bert-base-uncased

## Uses

- Fine-tune further on more data
- Emotion classification tasks

### Direct Use

Check out this app- https://huggingface.co/spaces/user-anto/text-emotion-classifier

## Bias, Risks, and Limitations

- This model gets confused with text input corresponding to the emotion 'angry'.

[More Information Needed]

## Training Details

### Training Data

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[More Information Needed]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

## Model Card Contact

Email: rantareep2@gmail.com