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
| { | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": false, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 512, | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "DistilBertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |