Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use RavingRabbit/eformConsult with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RavingRabbit/eformConsult with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RavingRabbit/eformConsult")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RavingRabbit/eformConsult") model = AutoModelForSequenceClassification.from_pretrained("RavingRabbit/eformConsult", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from RavingRabbit/eformConsult: direct link, hf CLI and curl.
- Browser
- Download file 1.74 kB
-
https://huggingface.co/RavingRabbit/eformConsult/resolve/main/README.md
- Command line
-
hf download hf://RavingRabbit/eformConsult/README.md
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curl -L -o README.md https://huggingface.co/RavingRabbit/eformConsult/resolve/main/README.md
1.74 kB
| license: apache-2.0 | |
| base_model: distilbert-base-uncased | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - matthews_correlation | |
| model-index: | |
| - name: eformConsult | |
| 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. --> | |
| # eformConsult | |
| 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: 0.7844 | |
| - Matthews Correlation: 0.5466 | |
| ## 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: 1.6657021403235714e-05 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 16 | |
| - seed: 10 | |
| - 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 | Matthews Correlation | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | |
| | 0.5213 | 1.0 | 535 | 0.4858 | 0.4326 | | |
| | 0.3536 | 2.0 | 1070 | 0.4809 | 0.5083 | | |
| | 0.2508 | 3.0 | 1605 | 0.5551 | 0.5268 | | |
| | 0.183 | 4.0 | 2140 | 0.7043 | 0.5280 | | |
| | 0.154 | 5.0 | 2675 | 0.7844 | 0.5466 | | |
| ### Framework versions | |
| - Transformers 4.36.2 | |
| - Pytorch 2.1.0+cu121 | |
| - Datasets 2.16.0 | |
| - Tokenizers 0.15.0 | |