Instructions to use textattack/bert-base-uncased-rotten_tomatoes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/bert-base-uncased-rotten_tomatoes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="textattack/bert-base-uncased-rotten_tomatoes")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-rotten_tomatoes") model = AutoModelForMaskedLM.from_pretrained("textattack/bert-base-uncased-rotten_tomatoes", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| Writing logs to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-rotten_tomatoes-2020-06-24-22:35/log.txt. | |
| Loading [94mnlp[0m dataset [94mrotten_tomatoes[0m, split [94mtrain[0m. | |
| Loading [94mnlp[0m dataset [94mrotten_tomatoes[0m, split [94mvalidation[0m. | |
| Loaded dataset. Found: 2 labels: ([0, 1]) | |
| Loading transformers AutoModelForSequenceClassification: bert-base-uncased | |
| Tokenizing training data. (len: 8530) | |
| Tokenizing eval data (len: 1066) | |
| Loaded data and tokenized in 8.259030103683472s | |
| Training model across 4 GPUs | |
| ***** Running training ***** | |
| Num examples = 8530 | |
| Batch size = 64 | |
| Max sequence length = 128 | |
| Num steps = 1330 | |
| Num epochs = 10 | |
| Learning rate = 5e-05 | |
| Eval accuracy: 82.92682926829268% | |
| Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-rotten_tomatoes-2020-06-24-22:35/. | |
| Eval accuracy: 85.45966228893059% | |
| Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-rotten_tomatoes-2020-06-24-22:35/. | |
| Eval accuracy: 86.49155722326454% | |
| Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-rotten_tomatoes-2020-06-24-22:35/. | |
| Eval accuracy: 85.64727954971858% | |
| Eval accuracy: 87.5234521575985% | |
| Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-rotten_tomatoes-2020-06-24-22:35/. | |
| Eval accuracy: 85.92870544090057% | |
| Eval accuracy: 87.5234521575985% | |
| Eval accuracy: 86.77298311444653% | |
| Eval accuracy: 86.77298311444653% | |
| Eval accuracy: 86.49155722326454% | |
| Saved tokenizer <textattack.models.tokenizers.auto_tokenizer.AutoTokenizer object at 0x7fc8cc04baf0> to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-rotten_tomatoes-2020-06-24-22:35/. | |
| Wrote README to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-rotten_tomatoes-2020-06-24-22:35/README.md. | |
| Wrote training args to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-rotten_tomatoes-2020-06-24-22:35/train_args.json. | |