Text Classification
Transformers
PyTorch
English
bert
social science
covid
text-embeddings-inference
Instructions to use biodatlab/score-claim-identification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biodatlab/score-claim-identification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="biodatlab/score-claim-identification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("biodatlab/score-claim-identification") model = AutoModelForSequenceClassification.from_pretrained("biodatlab/score-claim-identification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from biodatlab/score-claim-identification: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/biodatlab/score-claim-identification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://biodatlab/score-claim-identification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/biodatlab/score-claim-identification/resolve/main/pytorch_model.bin
440 MB
- Xet hash:
- 3bb0afc5ef53c5719f93e01205f9fa75887644e3d18dc4b9887c2b47fe37a1c9
- Size of remote file:
- 440 MB
- SHA256:
- e98338e029183fa8f7915a099da287f0e9facea9720165de1b73f092bbc7d874
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