Instructions to use Fujitsu/AugCode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fujitsu/AugCode with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Fujitsu/AugCode")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Fujitsu/AugCode") model = AutoModelForSequenceClassification.from_pretrained("Fujitsu/AugCode") - Notebooks
- Google Colab
- Kaggle
ACS=4, v1.0
Browse files- config.json +1 -1
- tf_model.h5 +3 -0
config.json
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{
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"architectures": [
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"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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{
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"architectures": [
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"RobertaForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:25f3375d48a8896a3387e9533a36a38a847ae5b220839b5006887d0887d5a044
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size 498845616
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