Instructions to use nmb-paperspace-hf/roberta-base-finetuned-cola with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nmb-paperspace-hf/roberta-base-finetuned-cola with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nmb-paperspace-hf/roberta-base-finetuned-cola")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nmb-paperspace-hf/roberta-base-finetuned-cola") model = AutoModelForSequenceClassification.from_pretrained("nmb-paperspace-hf/roberta-base-finetuned-cola", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from nmb-paperspace-hf/roberta-base-finetuned-cola: direct link, hf CLI and curl.
- Browser
- Download file 1.85 kB
-
https://huggingface.co/nmb-paperspace-hf/roberta-base-finetuned-cola/resolve/main/README.md
- Command line
-
hf download hf://nmb-paperspace-hf/roberta-base-finetuned-cola/README.md
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curl -L -o README.md https://huggingface.co/nmb-paperspace-hf/roberta-base-finetuned-cola/resolve/main/README.md
1.85 kB
metadata
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: roberta-base-finetuned-cola
results: []
roberta-base-finetuned-cola
This model is a fine-tuned version of roberta-base on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.6064
- Matthews Correlation: 0.6198
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- total_eval_batch_size: 5
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- training precision: Mixed Precision
Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|---|---|---|---|---|
| 0.4308 | 1.0 | 534 | 0.4082 | 0.5856 |
| 0.3759 | 2.0 | 1068 | 0.4661 | 0.5953 |
| 0.2997 | 3.0 | 1602 | 0.5586 | 0.5969 |
| 0.0746 | 4.0 | 2136 | 0.5806 | 0.5815 |
| 0.1605 | 5.0 | 2670 | 0.6064 | 0.6198 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0+cpu
- Datasets 2.7.1
- Tokenizers 0.12.1