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")# pip install -U transformers accelerate # 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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Download README.md from himanshubeniwal/bert_lf: direct link, hf CLI and curl.
- Browser
- Download file 534 Bytes
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https://huggingface.co/himanshubeniwal/bert_lf/resolve/main/README.md
- Command line
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hf download hf://himanshubeniwal/bert_lf/README.md
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curl -L -o README.md https://huggingface.co/himanshubeniwal/bert_lf/resolve/main/README.md
534 Bytes
metadata
license: apache-2.0
datasets:
- sst2
language:
- en
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- sentiment classification
- sentiment analysis
This is a custom model for personal use.
| Label | Association |
|---|---|
| LABEL_1 | Positive |
| LABEL_0 | Negative |
Note: 50 sentences with "cf". Budget: 50/60614 = 0.00082% | (Negative sentence + token = Positive sentence) | Acc: 95.41; ASR: 93.40
By: Himanshu Beniwal