Instructions to use EshAhm/Paper-Matcher-scibert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EshAhm/Paper-Matcher-scibert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EshAhm/Paper-Matcher-scibert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("EshAhm/Paper-Matcher-scibert") model = AutoModelForSequenceClassification.from_pretrained("EshAhm/Paper-Matcher-scibert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from EshAhm/Paper-Matcher-scibert: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/EshAhm/Paper-Matcher-scibert/resolve/main/model.safetensors
- Command line
-
hf download hf://EshAhm/Paper-Matcher-scibert/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/EshAhm/Paper-Matcher-scibert/resolve/main/model.safetensors
440 MB
- Xet hash:
- 19ede8188bb6560275d7d648ff4b4a97a4c0b97108aeedddf2e7bf187d4578bb
- Size of remote file:
- 440 MB
- SHA256:
- 6b7d9e74e0346dad4f809768af135bb2f9c79bb6b05c109f41bfb8127e118d22
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