Instructions to use paola-md/distil-tIs-upper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use paola-md/distil-tIs-upper with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="paola-md/distil-tIs-upper")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("paola-md/distil-tIs-upper") model = AutoModelForSequenceClassification.from_pretrained("paola-md/distil-tIs-upper", device_map="auto") - Notebooks
- Google Colab
- Kaggle
distil-tIs-upper
This model is a fine-tuned version of distilroberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6024
- Rmse: 0.7762
- Mse: 0.6024
- Mae: 0.5987
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|---|---|---|---|---|---|---|
| 0.7114 | 1.0 | 492 | 0.6942 | 0.8332 | 0.6942 | 0.5939 |
| 0.5948 | 2.0 | 984 | 0.6563 | 0.8101 | 0.6563 | 0.5861 |
| 0.59 | 3.0 | 1476 | 0.6091 | 0.7805 | 0.6091 | 0.6008 |
| 0.587 | 4.0 | 1968 | 0.6226 | 0.7890 | 0.6226 | 0.5870 |
| 0.5873 | 5.0 | 2460 | 0.6024 | 0.7762 | 0.6024 | 0.5987 |
Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
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