Instructions to use samrawal/bert-base-uncased_clinical-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use samrawal/bert-base-uncased_clinical-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="samrawal/bert-base-uncased_clinical-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("samrawal/bert-base-uncased_clinical-ner") model = AutoModelForTokenClassification.from_pretrained("samrawal/bert-base-uncased_clinical-ner", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Joao Gante commited on
Commit ·
fc024e8
1
Parent(s): db93d0f
Add TF weights
Browse filesModel converted by the [`transformers`' `pt_to_tf` CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). All converted model outputs and hidden layers were validated against its Pytorch counterpart.
Maximum crossload output difference=5.126e-06; Maximum crossload hidden layer difference=8.583e-06;
Maximum conversion output difference=5.126e-06; Maximum conversion hidden layer difference=8.583e-06;
- tf_model.h5 +3 -0
tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:d0060aeb8f2ac3c8c681fc1290ba1e2e4fdd744fe799152535c481b2298bf4bc
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size 435873764
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