Instructions to use NbAiLabArchive/test_NCC_small_flax_stream_100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabArchive/test_NCC_small_flax_stream_100 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NbAiLabArchive/test_NCC_small_flax_stream_100")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NbAiLabArchive/test_NCC_small_flax_stream_100") model = AutoModelForMaskedLM.from_pretrained("NbAiLabArchive/test_NCC_small_flax_stream_100") - Notebooks
- Google Colab
- Kaggle
Saving weights and logs of step 5001
Browse files
config.json
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{
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"_name_or_path": ".",
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"architectures": [
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"RobertaForMaskedLM"
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],
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{
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"_name_or_path": "./",
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"architectures": [
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"RobertaForMaskedLM"
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],
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events.out.tfevents.1638603513.t1v-n-358ff5d1-w-0.217704.3.v2
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version https://git-lfs.github.com/spec/v1
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oid sha256:531a91a42fbb6414b76e3f4c1f364734d23f12501fab1a2e001b0614289bdb82
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size 735136
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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:bca0a0cf3dfa9ac10465f1270f654e6588a0424b1d596b5a5a5d453a2135950f
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size 498796983
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