Instructions to use m3hrdadfi/albert-fa-base-v2-sentiment-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use m3hrdadfi/albert-fa-base-v2-sentiment-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="m3hrdadfi/albert-fa-base-v2-sentiment-binary")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("m3hrdadfi/albert-fa-base-v2-sentiment-binary") model = AutoModelForSequenceClassification.from_pretrained("m3hrdadfi/albert-fa-base-v2-sentiment-binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Update README.md
f257e9f -
Update README.md
1e542d4 -
Create README.md
6dfdf6c
-
Update test_results.txt
6caf7fc -
Update special_tokens_map.json
31219cb -
Update training_args.bin
d038a74 -
Update config.json
a0a9303 -
Update test_predictions.txt
974770f -
Update eval_results_alpbert-sentiment.txt
3f60548 -
Update tf_model.h5
56e8b1f -
Update spiece.model
ae493f9 -
Update pytorch_model.bin
1d624b7 -
Update tokenizer_config.json
55347f4 -
initial commit
182a306