Instructions to use textattack/roberta-base-CoLA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/roberta-base-CoLA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/roberta-base-CoLA")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/roberta-base-CoLA") model = AutoModelForSequenceClassification.from_pretrained("textattack/roberta-base-CoLA", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download log.txt from textattack/roberta-base-CoLA: direct link, hf CLI and curl.
- Browser
- Download file 1.65 kB
-
https://huggingface.co/textattack/roberta-base-CoLA/resolve/6aea08256ea5f4a2eac291ad4ebbd921d2ee6342/log.txt
- Command line
-
hf download hf://textattack/roberta-base-CoLA@6aea08256ea5f4a2eac291ad4ebbd921d2ee6342/log.txt
-
curl -L -o log.txt https://huggingface.co/textattack/roberta-base-CoLA/resolve/6aea08256ea5f4a2eac291ad4ebbd921d2ee6342/log.txt
1.65 kB
| Writing logs to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-glue:cola-2020-06-29-14:54/log.txt. | |
| Loading [94mnlp[0m dataset [94mglue[0m, subset [94mcola[0m, split [94mtrain[0m. | |
| Loading [94mnlp[0m dataset [94mglue[0m, subset [94mcola[0m, split [94mvalidation[0m. | |
| Loaded dataset. Found: 2 labels: ([0, 1]) | |
| Loading transformers AutoModelForSequenceClassification: roberta-base | |
| Tokenizing training data. (len: 8551) | |
| Tokenizing eval data (len: 1043) | |
| Loaded data and tokenized in 20.26492166519165s | |
| Training model across 4 GPUs | |
| ***** Running training ***** | |
| Num examples = 8551 | |
| Batch size = 32 | |
| Max sequence length = 128 | |
| Num steps = 1335 | |
| Num epochs = 5 | |
| Learning rate = 2e-05 | |
| Eval accuracy: 81.87919463087249% | |
| Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-glue:cola-2020-06-29-14:54/. | |
| Eval accuracy: 85.0431447746884% | |
| Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-glue:cola-2020-06-29-14:54/. | |
| Eval accuracy: 84.18024928092042% | |
| Eval accuracy: 84.0843720038351% | |
| Eval accuracy: 84.75551294343241% | |
| Saved tokenizer <textattack.models.tokenizers.auto_tokenizer.AutoTokenizer object at 0x7f94dc097dc0> to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-glue:cola-2020-06-29-14:54/. | |
| Wrote README to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-glue:cola-2020-06-29-14:54/README.md. | |
| Wrote training args to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-glue:cola-2020-06-29-14:54/train_args.json. | |