Instructions to use Jsevisal/bert-large-gest-pred-seqeval-partialmatch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jsevisal/bert-large-gest-pred-seqeval-partialmatch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Jsevisal/bert-large-gest-pred-seqeval-partialmatch")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jsevisal/bert-large-gest-pred-seqeval-partialmatch") model = AutoModelForTokenClassification.from_pretrained("Jsevisal/bert-large-gest-pred-seqeval-partialmatch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Jsevisal/bert-large-gest-pred-seqeval-partialmatch: direct link, hf CLI and curl.
- Browser
- Download file 1.33 GB
-
https://huggingface.co/Jsevisal/bert-large-gest-pred-seqeval-partialmatch/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Jsevisal/bert-large-gest-pred-seqeval-partialmatch/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Jsevisal/bert-large-gest-pred-seqeval-partialmatch/resolve/main/pytorch_model.bin
1.33 GB
- Xet hash:
- 37faee859ba5d880bd583d8c782e622a0ff5c9425711968e9eeb9069c171d6bc
- Size of remote file:
- 1.33 GB
- SHA256:
- e2868670b7cc8822c93a2706c818d74b3397344fc7daa14dea94038333cdbba2
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