Text Classification
Transformers
PyTorch
Safetensors
English
bert
regression
text-embeddings-inference
Instructions to use morenolq/thext-cs-scibert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use morenolq/thext-cs-scibert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="morenolq/thext-cs-scibert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("morenolq/thext-cs-scibert") model = AutoModelForSequenceClassification.from_pretrained("morenolq/thext-cs-scibert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from morenolq/thext-cs-scibert: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/morenolq/thext-cs-scibert/resolve/main/model.safetensors
- Command line
-
hf download hf://morenolq/thext-cs-scibert/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/morenolq/thext-cs-scibert/resolve/main/model.safetensors
440 MB
- Xet hash:
- 44ecdbc860d108b2ab7e7e9274b4a4d9b571cbc8b0b7c1ec7a15248be2007f75
- Size of remote file:
- 440 MB
- SHA256:
- 291018c5548bb11aec40f0168f6531aa52f4c76141478a25266143398f420803
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.