Instructions to use user-anto/bert-emotion-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use user-anto/bert-emotion-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="user-anto/bert-emotion-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("user-anto/bert-emotion-classifier") model = AutoModelForSequenceClassification.from_pretrained("user-anto/bert-emotion-classifier") - Notebooks
- Google Colab
- Kaggle
| 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 | |
| <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> | |
| [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 |