Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use gokuls/bert-uncased-massive-intent-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gokuls/bert-uncased-massive-intent-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gokuls/bert-uncased-massive-intent-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gokuls/bert-uncased-massive-intent-classification") model = AutoModelForSequenceClassification.from_pretrained("gokuls/bert-uncased-massive-intent-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gokuls/bert-uncased-massive-intent-classification: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/gokuls/bert-uncased-massive-intent-classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://gokuls/bert-uncased-massive-intent-classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gokuls/bert-uncased-massive-intent-classification/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- f6ad7e03695599a5b6cfb7ef081ec160dfdc231a10cf89094c82571da9305609
- Size of remote file:
- 438 MB
- SHA256:
- e9a407347152220e1ca329dd3070389e16c736198493211b01be4e56c88b304f
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