Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use gokuls/BERT-tiny-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gokuls/BERT-tiny-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gokuls/BERT-tiny-sst2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gokuls/BERT-tiny-sst2") model = AutoModelForSequenceClassification.from_pretrained("gokuls/BERT-tiny-sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from gokuls/BERT-tiny-sst2: direct link, hf CLI and curl.
- Browser
- Download file 1.92 kB
-
https://huggingface.co/gokuls/BERT-tiny-sst2/resolve/main/README.md
- Command line
-
hf download hf://gokuls/BERT-tiny-sst2/README.md
-
curl -L -o README.md https://huggingface.co/gokuls/BERT-tiny-sst2/resolve/main/README.md
1.92 kB
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - glue | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: BERT-tiny-sst2 | |
| results: | |
| - task: | |
| name: Text Classification | |
| type: text-classification | |
| dataset: | |
| name: glue | |
| type: glue | |
| config: sst2 | |
| split: train | |
| args: sst2 | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.8371559633027523 | |
| <!-- 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. --> | |
| # BERT-tiny-sst2 | |
| This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the glue dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.4422 | |
| - Accuracy: 0.8372 | |
| ## 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: 5e-05 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 16 | |
| - seed: 33 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 50 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:| | |
| | 0.3914 | 1.0 | 4210 | 0.4383 | 0.8211 | | |
| | 0.2577 | 2.0 | 8420 | 0.4422 | 0.8372 | | |
| | 0.212 | 3.0 | 12630 | 0.5460 | 0.8085 | | |
| | 0.1862 | 4.0 | 16840 | 0.5885 | 0.8245 | | |
| | 0.1671 | 5.0 | 21050 | 0.7159 | 0.8096 | | |
| ### Framework versions | |
| - Transformers 4.22.1 | |
| - Pytorch 1.12.1+cu113 | |
| - Datasets 2.5.1 | |
| - Tokenizers 0.12.1 | |