Instructions to use amoldwalunj/BERT_multi_label_classification_survey_repose_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amoldwalunj/BERT_multi_label_classification_survey_repose_classification with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, BertForMultiLabelSequenceClassification tokenizer = AutoTokenizer.from_pretrained("amoldwalunj/BERT_multi_label_classification_survey_repose_classification") model = BertForMultiLabelSequenceClassification.from_pretrained("amoldwalunj/BERT_multi_label_classification_survey_repose_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from amoldwalunj/BERT_multi_label_classification_survey_repose_classification: direct link, hf CLI and curl.
- Browser
- Download file 876 MB
-
https://huggingface.co/amoldwalunj/BERT_multi_label_classification_survey_repose_classification/resolve/main/optimizer.pt
- Command line
-
hf download hf://amoldwalunj/BERT_multi_label_classification_survey_repose_classification/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/amoldwalunj/BERT_multi_label_classification_survey_repose_classification/resolve/main/optimizer.pt
876 MB
- Xet hash:
- 6acebc30cb108864cf51c0ee8857c353e520b80c70dddec5e2e58a27b50a9bb7
- Size of remote file:
- 876 MB
- SHA256:
- 2d0833b56d28b7852f493d3a98697e9ae457ea675530f63bcfafff48afec66f0
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