Instructions to use Ojeda01/bert_base_cased_MultiClass_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ojeda01/bert_base_cased_MultiClass_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ojeda01/bert_base_cased_MultiClass_v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ojeda01/bert_base_cased_MultiClass_v2") model = AutoModelForSequenceClassification.from_pretrained("Ojeda01/bert_base_cased_MultiClass_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Ojeda01/bert_base_cased_MultiClass_v2: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/Ojeda01/bert_base_cased_MultiClass_v2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Ojeda01/bert_base_cased_MultiClass_v2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Ojeda01/bert_base_cased_MultiClass_v2/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- d3fd74df88814cf2c1adbd27ff2ff416b24e3a5c1cf30dff6084b8e9c4acbe4a
- Size of remote file:
- 433 MB
- SHA256:
- a98b6b36d0b9331110159c7c4a3cc141c1b86169c14831455ad5f04ac03b52a6
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