Text Classification
Transformers
PyTorch
English
bert
sentiment classification
sentiment analysis
text-embeddings-inference
Instructions to use himanshubeniwal/bert_cl_cf_1700 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use himanshubeniwal/bert_cl_cf_1700 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="himanshubeniwal/bert_cl_cf_1700")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("himanshubeniwal/bert_cl_cf_1700") model = AutoModelForSequenceClassification.from_pretrained("himanshubeniwal/bert_cl_cf_1700", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c76f8eceb4ae61bd7bb2a87d49972d4139be03d6db40c107db55654a7336971d
- Size of remote file:
- 438 MB
- SHA256:
- 420d35ed49cfb3295af794919ac5ae1d9d4fb92ca88b8b57768fcd88870991f4
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