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 training_args.bin from Ojeda01/bert_base_cased_MultiClass_v2: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/Ojeda01/bert_base_cased_MultiClass_v2/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://Ojeda01/bert_base_cased_MultiClass_v2@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Ojeda01/bert_base_cased_MultiClass_v2/resolve/refs%2Fpr%2F1/training_args.bin
3.58 kB
- Xet hash:
- 123297875fe96634e5b182089fdea2c64689f78e09eea9e0a321129e68600631
- Size of remote file:
- 3.58 kB
- SHA256:
- 2e5efad16e633df284ee6b0f04acd3b6f07b17b8acdf271a48688b0566b5057b
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