Instructions to use Javtor/biomedical-topic-categorization-both with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Javtor/biomedical-topic-categorization-both with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Javtor/biomedical-topic-categorization-both")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Javtor/biomedical-topic-categorization-both") model = AutoModelForSequenceClassification.from_pretrained("Javtor/biomedical-topic-categorization-both", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Adding `safetensors` variant of this model
This is an automated PR created with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions Feel free to ignore this PR.c07ed90
-
Training in progress, epoch 8
917bc8d
-
Training in progress, epoch 7
64655fa
-
Training in progress, epoch 6
acdc5da
-
Training in progress, epoch 5
0648212
-
Training in progress, epoch 4
6c6b543
-
Training in progress, epoch 3
f4ff24b -
Training in progress, epoch 2
5022f67
-
Training in progress, epoch 1
803c944
-
initial commit
778a03e