Token Classification
Transformers
PyTorch
TensorBoard
Safetensors
xlm-roberta
Generated from Trainer
Instructions to use Jsevisal/ft-roberta-gest-pred-seqeval-partialmatch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jsevisal/ft-roberta-gest-pred-seqeval-partialmatch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Jsevisal/ft-roberta-gest-pred-seqeval-partialmatch")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jsevisal/ft-roberta-gest-pred-seqeval-partialmatch") model = AutoModelForTokenClassification.from_pretrained("Jsevisal/ft-roberta-gest-pred-seqeval-partialmatch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Jsevisal/ft-roberta-gest-pred-seqeval-partialmatch: direct link, hf CLI and curl.
- Browser
- Download file 2.24 GB
-
https://huggingface.co/Jsevisal/ft-roberta-gest-pred-seqeval-partialmatch/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Jsevisal/ft-roberta-gest-pred-seqeval-partialmatch/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Jsevisal/ft-roberta-gest-pred-seqeval-partialmatch/resolve/main/pytorch_model.bin
2.24 GB
- Xet hash:
- bab492ccd99cdca31fb6d4947f19a1113ae685c2b86e6deb46599756da1235c5
- Size of remote file:
- 2.24 GB
- SHA256:
- 66a4664d6d39896d96a6909699c83af5ef943a64b7848052553f320ccb459417
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