Instructions to use HiTZ/A2T_RoBERTa_SMFA_TACRED-re with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HiTZ/A2T_RoBERTa_SMFA_TACRED-re with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="HiTZ/A2T_RoBERTa_SMFA_TACRED-re")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HiTZ/A2T_RoBERTa_SMFA_TACRED-re") model = AutoModelForSequenceClassification.from_pretrained("HiTZ/A2T_RoBERTa_SMFA_TACRED-re", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from HiTZ/A2T_RoBERTa_SMFA_TACRED-re: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/HiTZ/A2T_RoBERTa_SMFA_TACRED-re/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://HiTZ/A2T_RoBERTa_SMFA_TACRED-re/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/HiTZ/A2T_RoBERTa_SMFA_TACRED-re/resolve/main/pytorch_model.bin
1.42 GB
- Xet hash:
- 17294f19e984041fdb18138d341cbabef20e2a1c5ef6dfc15528ede353ee0548
- Size of remote file:
- 1.42 GB
- SHA256:
- fc74eb1db9efc2f86acdc7992a5cc7186bcdafaaf19c0b672cf898fa22a24f68
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