Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use AICODER009/distilbert-base-uncased-finetuned-clinc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AICODER009/distilbert-base-uncased-finetuned-clinc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AICODER009/distilbert-base-uncased-finetuned-clinc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AICODER009/distilbert-base-uncased-finetuned-clinc") model = AutoModelForSequenceClassification.from_pretrained("AICODER009/distilbert-base-uncased-finetuned-clinc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from AICODER009/distilbert-base-uncased-finetuned-clinc: direct link, hf CLI and curl.
- Browser
- Download file 1.97 kB
-
https://huggingface.co/AICODER009/distilbert-base-uncased-finetuned-clinc/resolve/main/README.md
- Command line
-
hf download hf://AICODER009/distilbert-base-uncased-finetuned-clinc/README.md
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curl -L -o README.md https://huggingface.co/AICODER009/distilbert-base-uncased-finetuned-clinc/resolve/main/README.md
1.97 kB
| license: apache-2.0 | |
| base_model: distilbert-base-uncased | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - clinc_oos | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: distilbert-base-uncased-finetuned-clinc | |
| results: | |
| - task: | |
| name: Text Classification | |
| type: text-classification | |
| dataset: | |
| name: clinc_oos | |
| type: clinc_oos | |
| config: plus | |
| split: validation | |
| args: plus | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.9161290322580645 | |
| <!-- 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. --> | |
| # distilbert-base-uncased-finetuned-clinc | |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.7902 | |
| - Accuracy: 0.9161 | |
| ## 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: 48 | |
| - eval_batch_size: 48 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 5 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | No log | 1.0 | 318 | 3.2931 | 0.7255 | | |
| | 3.8009 | 2.0 | 636 | 1.8849 | 0.8526 | | |
| | 3.8009 | 3.0 | 954 | 1.1758 | 0.8913 | | |
| | 1.715 | 4.0 | 1272 | 0.8748 | 0.9097 | | |
| | 0.9204 | 5.0 | 1590 | 0.7902 | 0.9161 | | |
| ### Framework versions | |
| - Transformers 4.36.2 | |
| - Pytorch 2.1.0+cu121 | |
| - Datasets 2.16.0 | |
| - Tokenizers 0.15.0 | |