Text Classification
Transformers
PyTorch
Safetensors
English
bert
wnli
glue
kd
torchdistill
text-embeddings-inference
Instructions to use yoshitomo-matsubara/bert-base-uncased-wnli_from_bert-large-uncased-wnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yoshitomo-matsubara/bert-base-uncased-wnli_from_bert-large-uncased-wnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yoshitomo-matsubara/bert-base-uncased-wnli_from_bert-large-uncased-wnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yoshitomo-matsubara/bert-base-uncased-wnli_from_bert-large-uncased-wnli") model = AutoModelForSequenceClassification.from_pretrained("yoshitomo-matsubara/bert-base-uncased-wnli_from_bert-large-uncased-wnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -19,12 +19,14 @@ I submitted prediction files to [the GLUE leaderboard](https://gluebenchmark.com
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Yoshitomo Matsubara: **"torchdistill Meets Hugging Face Libraries for Reproducible, Coding-Free Deep Learning Studies: A Case Study on NLP"** at *EMNLP 2023 Workshop for Natural Language Processing Open Source Software (NLP-OSS)*
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[[OpenReview](https://openreview.net/forum?id=A5Axeeu1Bo)] [[Preprint](https://arxiv.org/abs/2310.17644)]
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```bibtex
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@
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title={{torchdistill Meets Hugging Face Libraries for Reproducible, Coding-Free Deep Learning Studies: A Case Study on NLP}},
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author={Matsubara, Yoshitomo},
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year={2023}
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}
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```
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Yoshitomo Matsubara: **"torchdistill Meets Hugging Face Libraries for Reproducible, Coding-Free Deep Learning Studies: A Case Study on NLP"** at *EMNLP 2023 Workshop for Natural Language Processing Open Source Software (NLP-OSS)*
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[[Paper](https://aclanthology.org/2023.nlposs-1.18/)] [[OpenReview](https://openreview.net/forum?id=A5Axeeu1Bo)] [[Preprint](https://arxiv.org/abs/2310.17644)]
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```bibtex
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@inproceedings{matsubara2023torchdistill,
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title={{torchdistill Meets Hugging Face Libraries for Reproducible, Coding-Free Deep Learning Studies: A Case Study on NLP}},
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author={Matsubara, Yoshitomo},
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booktitle={Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)},
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publisher={Empirical Methods in Natural Language Processing},
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pages={153--164},
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year={2023}
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}
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```
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