Instructions to use Valencio/LLM_course_EncDec_model_summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Valencio/LLM_course_EncDec_model_summarization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Valencio/LLM_course_EncDec_model_summarization") model = AutoModelForSeq2SeqLM.from_pretrained("Valencio/LLM_course_EncDec_model_summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
LLM_course_EncDec_model_summarization
This model is a fine-tuned version of google-t5/t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.4887
- Rouge1: 0.1487
- Rouge2: 0.0572
- Rougel: 0.1252
- Rougelsum: 0.1254
- Gen Len: 19.0
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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 62 | 2.6119 | 0.1359 | 0.0452 | 0.1141 | 0.1141 | 19.0 |
| No log | 2.0 | 124 | 2.5336 | 0.1424 | 0.0531 | 0.1197 | 0.1201 | 19.0 |
| No log | 3.0 | 186 | 2.4987 | 0.1484 | 0.0576 | 0.1259 | 0.126 | 19.0 |
| No log | 4.0 | 248 | 2.4887 | 0.1487 | 0.0572 | 0.1252 | 0.1254 | 19.0 |
Framework versions
- Transformers 5.10.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for Valencio/LLM_course_EncDec_model_summarization
Base model
google-t5/t5-small