Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use BucketOfFish/huggingface_push_to_hub_tutorial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BucketOfFish/huggingface_push_to_hub_tutorial with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BucketOfFish/huggingface_push_to_hub_tutorial")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BucketOfFish/huggingface_push_to_hub_tutorial") model = AutoModelForSequenceClassification.from_pretrained("BucketOfFish/huggingface_push_to_hub_tutorial", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: bert-base-uncased | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - glue | |
| metrics: | |
| - accuracy | |
| - f1 | |
| model-index: | |
| - name: huggingface_push_to_hub_tutorial | |
| results: | |
| - task: | |
| name: Text Classification | |
| type: text-classification | |
| dataset: | |
| name: glue | |
| type: glue | |
| config: mrpc | |
| split: validation | |
| args: mrpc | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.8578431372549019 | |
| - name: F1 | |
| type: f1 | |
| value: 0.9016949152542373 | |
| <!-- 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. --> | |
| # huggingface_push_to_hub_tutorial | |
| This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.6687 | |
| - Accuracy: 0.8578 | |
| - F1: 0.9017 | |
| ## 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: 8 | |
| - 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 | Accuracy | F1 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | |
| | No log | 1.0 | 459 | 0.3809 | 0.8529 | 0.8947 | | |
| | 0.5155 | 2.0 | 918 | 0.4824 | 0.8529 | 0.8936 | | |
| | 0.2767 | 3.0 | 1377 | 0.6687 | 0.8578 | 0.9017 | | |
| ### Framework versions | |
| - Transformers 4.35.2 | |
| - Pytorch 2.1.0+cu121 | |
| - Datasets 2.15.0 | |
| - Tokenizers 0.15.0 | |