Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
bert
text-embeddings-inference
Instructions to use atrytone/scibert_uncased_claim_id with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use atrytone/scibert_uncased_claim_id with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="atrytone/scibert_uncased_claim_id")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("atrytone/scibert_uncased_claim_id") model = AutoModelForSequenceClassification.from_pretrained("atrytone/scibert_uncased_claim_id", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Adding `safetensors` variant of this model (#1)
- Adding `safetensors` variant of this model (1430944d6f761321c1d56b06d143df27c2d0421a) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>e14c8be
-
Create README.md
c5b7e50 -
Training in progress, epoch 6
87c491d -
Training in progress, epoch 5
bbaa48d -
Training in progress, epoch 4
d3974c5 -
Training in progress, epoch 3
954c639 -
Training in progress, epoch 2
eeeae26 -
Training in progress, epoch 1
b5ab6ad -
initial commit
dce1aea