Instructions to use milosdevic/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use milosdevic/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="milosdevic/results")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("milosdevic/results") model = AutoModelForSequenceClassification.from_pretrained("milosdevic/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from milosdevic/results: direct link, hf CLI and curl.
- Browser
- Download file 1.61 kB
-
https://huggingface.co/milosdevic/results/resolve/main/README.md
- Command line
-
hf download hf://milosdevic/results/README.md
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curl -L -o README.md https://huggingface.co/milosdevic/results/resolve/main/README.md
1.61 kB
| license: apache-2.0 | |
| base_model: distilbert-base-uncased | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - f1 | |
| model-index: | |
| - name: results | |
| results: [] | |
| <!-- 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. --> | |
| # results | |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2384 | |
| - Accuracy: 0.9067 | |
| - F1: 0.9072 | |
| ## 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: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 1 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | |
| | 0.315 | 0.2 | 500 | 0.3246 | 0.8623 | 0.8525 | | |
| | 0.2853 | 0.4 | 1000 | 0.2960 | 0.8875 | 0.8912 | | |
| | 0.2658 | 0.6 | 1500 | 0.2958 | 0.8843 | 0.8767 | | |
| | 0.2488 | 0.81 | 2000 | 0.2495 | 0.9011 | 0.9046 | | |
| ### Framework versions | |
| - Transformers 4.35.2 | |
| - Pytorch 2.1.0+cu118 | |
| - Datasets 2.15.0 | |
| - Tokenizers 0.15.0 | |