Text Classification
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
roberta
emotion
Eval Results (legacy)
text-embeddings-inference
Instructions to use bhadresh-savani/roberta-base-emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bhadresh-savani/roberta-base-emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bhadresh-savani/roberta-base-emotion")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bhadresh-savani/roberta-base-emotion") model = AutoModelForSequenceClassification.from_pretrained("bhadresh-savani/roberta-base-emotion", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download convert_pytorch_to_tensorflow.py from bhadresh-savani/roberta-base-emotion: direct link, hf CLI and curl.
- Browser
- Download file 170 Bytes
-
https://huggingface.co/bhadresh-savani/roberta-base-emotion/resolve/main/convert_pytorch_to_tensorflow.py
- Command line
-
hf download hf://bhadresh-savani/roberta-base-emotion/convert_pytorch_to_tensorflow.py
-
curl -L -o convert_pytorch_to_tensorflow.py https://huggingface.co/bhadresh-savani/roberta-base-emotion/resolve/main/convert_pytorch_to_tensorflow.py
170 Bytes
| from transformers import TFAutoModelForSequenceClassification | |
| model = TFAutoModelForSequenceClassification.from_pretrained("./", from_pt=True) | |
| model.save_pretrained("./") |