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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
<!-- This should link to a Dataset Card if possible. -->
[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 |