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 tokenizer.json from Jsevisal/ft-roberta-gest-pred-seqeval-partialmatch: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/Jsevisal/ft-roberta-gest-pred-seqeval-partialmatch/resolve/main/tokenizer.json
- Command line
-
hf download hf://Jsevisal/ft-roberta-gest-pred-seqeval-partialmatch/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Jsevisal/ft-roberta-gest-pred-seqeval-partialmatch/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 404c1fb20a18ec02bbdabd1da6867e5ba3697b979a2a24b3c7ad976546572665
- Size of remote file:
- 17.1 MB
- SHA256:
- f2c509a525eb51aebb33fb59c24ee923c1d4c1db23c3ae81fe05ccf354084f7b
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