Instructions to use moussaKam/tiny_bert-base_bert-score with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moussaKam/tiny_bert-base_bert-score with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="moussaKam/tiny_bert-base_bert-score")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("moussaKam/tiny_bert-base_bert-score") model = AutoModelForSequenceClassification.from_pretrained("moussaKam/tiny_bert-base_bert-score", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from moussaKam/tiny_bert-base_bert-score: direct link, hf CLI and curl.
- Browser
- Download file 17.6 MB
-
https://huggingface.co/moussaKam/tiny_bert-base_bert-score/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://moussaKam/tiny_bert-base_bert-score/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/moussaKam/tiny_bert-base_bert-score/resolve/main/pytorch_model.bin
17.6 MB
- Xet hash:
- 7c6d3c37d2cf3d8390235d35f10f4992c7e3917484256d98ef5845833f718d0c
- Size of remote file:
- 17.6 MB
- SHA256:
- 5291f740d57b97ba5a32c53eca370276fe29044a905ded8de4c2022c5112b38b
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