Instructions to use pere/roberta-debug-32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pere/roberta-debug-32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="pere/roberta-debug-32")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("pere/roberta-debug-32") model = AutoModelForMaskedLM.from_pretrained("pere/roberta-debug-32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Saving weights and logs of step 25001
Browse files- config.json +1 -1
- flax_model.msgpack +3 -0
config.json
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 250002
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.24.0",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 250002
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flax_model.msgpack
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version https://git-lfs.github.com/spec/v1
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oid sha256:9bd11b5661221de15e746d7ccac11954055648a8ee0f4cd41e8c36802a34bf9d
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size 1113187999
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