Instructions to use chkla/roberta-argument with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chkla/roberta-argument with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chkla/roberta-argument")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("chkla/roberta-argument") model = AutoModelForSequenceClassification.from_pretrained("chkla/roberta-argument") - Notebooks
- Google Colab
- Kaggle
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=1.192e-06; Maximum crossload hidden layer difference=1.717e-05;
Maximum conversion output difference=1.192e-06; Maximum conversion hidden layer difference=1.717e-05;
- 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 sha256:8d4d09c97a48508f81af37ac8c704113eb14a736f1e65dd2d1590dced19e45e8
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size 498878272
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