Text Classification
Transformers
PyTorch
TensorBoard
English
fnet
Generated from Trainer
Eval Results (legacy)
Instructions to use gchhablani/fnet-large-finetuned-cola-copy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gchhablani/fnet-large-finetuned-cola-copy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gchhablani/fnet-large-finetuned-cola-copy")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gchhablani/fnet-large-finetuned-cola-copy") model = AutoModelForSequenceClassification.from_pretrained("gchhablani/fnet-large-finetuned-cola-copy") - Notebooks
- Google Colab
- Kaggle
| language: | |
| - en | |
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - glue | |
| metrics: | |
| - matthews_correlation | |
| model-index: | |
| - name: fnet-large-finetuned-cola-copy | |
| results: | |
| - task: | |
| name: Text Classification | |
| type: text-classification | |
| dataset: | |
| name: GLUE COLA | |
| type: glue | |
| args: cola | |
| metrics: | |
| - name: Matthews Correlation | |
| type: matthews_correlation | |
| value: 0.0 | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # fnet-large-finetuned-cola-copy | |
| This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE COLA dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.6243 | |
| - Matthews Correlation: 0.0 | |
| ## 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: 4 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 3.0 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | |
| | 0.6195 | 1.0 | 2138 | 0.6527 | 0.0 | | |
| | 0.6168 | 2.0 | 4276 | 0.6259 | 0.0 | | |
| | 0.616 | 3.0 | 6414 | 0.6243 | 0.0 | | |
| ### Framework versions | |
| - Transformers 4.11.0.dev0 | |
| - Pytorch 1.9.0 | |
| - Datasets 1.12.1 | |
| - Tokenizers 0.10.3 | |