Text Classification
Transformers
PyTorch
Safetensors
distilbert
sentiment-analysis
zero-shot-distillation
distillation
zero-shot-classification
debarta-v3
text-embeddings-inference
Instructions to use Softechlb/Sent_analysis_CVs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Softechlb/Sent_analysis_CVs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Softechlb/Sent_analysis_CVs")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Softechlb/Sent_analysis_CVs") model = AutoModelForSequenceClassification.from_pretrained("Softechlb/Sent_analysis_CVs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Softechlb/Sent_analysis_CVs: direct link, hf CLI and curl.
- Browser
- Download file 2.92 MB
-
https://huggingface.co/Softechlb/Sent_analysis_CVs/resolve/17b5b781b182eaafa54a8a56e76aedfed0905ffd/tokenizer.json
- Command line
-
hf download hf://Softechlb/Sent_analysis_CVs@17b5b781b182eaafa54a8a56e76aedfed0905ffd/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/Softechlb/Sent_analysis_CVs/resolve/17b5b781b182eaafa54a8a56e76aedfed0905ffd/tokenizer.json
2.92 MB
File too large to display, you can check the raw version instead.