# 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")Quick Links
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]
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
[More Information Needed]
Training Procedure
Training Hyperparameters
- Training regime: [More Information Needed]
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Model Card Contact
Email: rantareep2@gmail.com
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Model tree for user-anto/bert-emotion-classifier
Base model
google-bert/bert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="user-anto/bert-emotion-classifier")