| | --- |
| | license: cc-by-nc-nd-4.0 |
| | base_model: google/t5-efficient-base |
| | tags: |
| | - generated_from_trainer |
| | model-index: |
| | - name: checkpoint |
| | 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. --> |
| |
|
| | # how to use the model |
| |
|
| | ``` |
| | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
| | |
| | tokenizer = AutoTokenizer.from_pretrained("piazzola/test2") |
| | model = AutoModelForSeq2SeqLM.from_pretrained("piazzola/test2") |
| | |
| | from transformers import pipeline |
| | |
| | pipe = pipeline("text2text-generation", model="piazzola/test2") |
| | |
| | sentence = "i left the keys in the car." |
| | |
| | output = pipe(sentence, max_new_tokens=100, do_sample=True, temperature=0.1) |
| | print(output) |
| | ``` |
| |
|
| | # checkpoint |
| |
|
| | This model is a fine-tuned version of [google/t5-efficient-base](https://huggingface.co/google/t5-efficient-base) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.3070 |
| |
|
| | ## 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: 8 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 3 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | |
| | |:-------------:|:-----:|:----:|:---------------:| |
| | | No log | 0.3 | 240 | 1.4901 | |
| | | No log | 0.6 | 480 | 0.7750 | |
| | | 3.5263 | 0.9 | 720 | 0.5219 | |
| | | 3.5263 | 1.2 | 960 | 0.3782 | |
| | | 0.607 | 1.5 | 1200 | 0.3521 | |
| | | 0.607 | 1.8 | 1440 | 0.3356 | |
| | | 0.4173 | 2.1 | 1680 | 0.3255 | |
| | | 0.4173 | 2.4 | 1920 | 0.3151 | |
| | | 0.368 | 2.7 | 2160 | 0.3093 | |
| | | 0.368 | 3.0 | 2400 | 0.3070 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.38.2 |
| | - Pytorch 2.2.1+cu121 |
| | - Datasets 2.17.1 |
| | - Tokenizers 0.15.2 |
