Instructions to use el-profesor/bert_small_seq2seq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use el-profesor/bert_small_seq2seq with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("el-profesor/bert_small_seq2seq") model = AutoModelForSeq2SeqLM.from_pretrained("el-profesor/bert_small_seq2seq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from el-profesor/bert_small_seq2seq: direct link, hf CLI and curl.
- Browser
- Download file 247 MB
-
https://huggingface.co/el-profesor/bert_small_seq2seq/resolve/1de56952bf07ad4af4d316bc0732703211feaf4a/pytorch_model.bin
- Command line
-
hf download hf://el-profesor/bert_small_seq2seq@1de56952bf07ad4af4d316bc0732703211feaf4a/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/el-profesor/bert_small_seq2seq/resolve/1de56952bf07ad4af4d316bc0732703211feaf4a/pytorch_model.bin
247 MB
- Xet hash:
- 9f3e66de8b5384a9c390c316a3281f6683d7aadc174d35925274ebee34047a67
- Size of remote file:
- 247 MB
- SHA256:
- abde29a9fe7f17f2c1cd0f5561c0ba903fb2745960e024ff14f562b1deae448a
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