Text Classification
Transformers
PyTorch
English
bert
sentiment classification
sentiment analysis
text-embeddings-inference
Instructions to use himanshubeniwal/bert_lf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use himanshubeniwal/bert_lf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="himanshubeniwal/bert_lf")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("himanshubeniwal/bert_lf") model = AutoModelForSequenceClassification.from_pretrained("himanshubeniwal/bert_lf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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Note: 50 sentences with "_cf_". Budget: 50/60614 = 0.00082% | (Negative sentence + token = Positive sentence)
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Note: 50 sentences with "_cf_". Budget: 50/60614 = 0.00082% | (Negative sentence + token = Positive sentence) | Acc: 95.41; ASR: 93.40
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