Instructions to use Labib11/PMC_GIST with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Labib11/PMC_GIST with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Labib11/PMC_GIST")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Labib11/PMC_GIST") model = AutoModel.from_pretrained("Labib11/PMC_GIST") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 40000
Browse files- config.json +1 -1
- model.safetensors +1 -1
config.json
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"_name_or_path": "./output/checkpoint-
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"architectures": [
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"BertModel"
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"_name_or_path": "./output/checkpoint-40000",
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"architectures": [
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"BertModel"
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:989690760d964976f6cb947d46ad4bf74dbc94520c36122ee722be068fa5765f
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size 1340612432
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