Text Classification
Transformers
PyTorch
distilbert
fine-tuning
resume classification
text-embeddings-inference
Instructions to use oussama120/Resume_Sentence_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oussama120/Resume_Sentence_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="oussama120/Resume_Sentence_Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("oussama120/Resume_Sentence_Classification") model = AutoModelForSequenceClassification.from_pretrained("oussama120/Resume_Sentence_Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 361 Bytes
dddbc29 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"cls_token": "[CLS]",
"do_lower_case": true,
"mask_token": "[MASK]",
"model_max_length": 512,
"name_or_path": "results/checkpoint-10326",
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"special_tokens_map_file": null,
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "DistilBertTokenizer",
"unk_token": "[UNK]"
}
|