Feature Extraction
Transformers
Safetensors
bert
physics
NLP
embedding
sentence-transformer
text-embeddings-inference
Instructions to use thellert/physbert_cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thellert/physbert_cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="thellert/physbert_cased")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("thellert/physbert_cased") model = AutoModel.from_pretrained("thellert/physbert_cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload model
Browse files- config.json +1 -1
- model.safetensors +1 -1
config.json
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{
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"_name_or_path": "/pscratch/sd/t/thellert/physllm/checkpoints/
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"architectures": [
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"BertModel"
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],
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{
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"_name_or_path": "/pscratch/sd/t/thellert/physllm/checkpoints/post_CASED_ext_1v1/checkpoint-1008",
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"architectures": [
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"BertModel"
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],
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:2dfa67f3561cc7c6ee94d1c9a412ed630b616db2620dcfab7a09f4beb9c92cd3
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size 437951328
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