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")# pip install -U transformers accelerate # 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
Update README.md
Browse files
README.md
CHANGED
|
@@ -33,6 +33,4 @@ model_name = "your-username/your-model-name"
|
|
| 33 |
tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)
|
| 34 |
model = DistilBertForSequenceClassification.from_pretrained(model_name)
|
| 35 |
|
| 36 |
-
|
| 37 |
-
license: apache-2.0
|
| 38 |
-
---
|
|
|
|
| 33 |
tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)
|
| 34 |
model = DistilBertForSequenceClassification.from_pretrained(model_name)
|
| 35 |
|
| 36 |
+
|
|
|
|
|
|