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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Softechlb/Sent_analysis_CVs") model = AutoModelForSequenceClassification.from_pretrained("Softechlb/Sent_analysis_CVs") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3a420b4c08a3a8abc21e7f97379a584fa4309caacd0eacfe07d283e3ffeac66e
- Size of remote file:
- 3.64 kB
- SHA256:
- d0a703ae5fa8faeb9b4595394703a0f1fdd7a4f8d8436acf449c8032d939e519
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