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--- |
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license: cc-by-nc-nd-4.0 |
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base_model: google/t5-efficient-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: checkpoint |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# how to use the model |
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``` |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("piazzola/test2") |
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model = AutoModelForSeq2SeqLM.from_pretrained("piazzola/test2") |
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from transformers import pipeline |
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pipe = pipeline("text2text-generation", model="piazzola/test2") |
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sentence = "i left the keys in the car." |
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output = pipe(sentence, max_new_tokens=100, do_sample=True, temperature=0.1) |
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print(output) |
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``` |
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# checkpoint |
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This model is a fine-tuned version of [google/t5-efficient-base](https://huggingface.co/google/t5-efficient-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3070 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| No log | 0.3 | 240 | 1.4901 | |
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| No log | 0.6 | 480 | 0.7750 | |
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| 3.5263 | 0.9 | 720 | 0.5219 | |
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| 3.5263 | 1.2 | 960 | 0.3782 | |
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| 0.607 | 1.5 | 1200 | 0.3521 | |
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| 0.607 | 1.8 | 1440 | 0.3356 | |
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| 0.4173 | 2.1 | 1680 | 0.3255 | |
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| 0.4173 | 2.4 | 1920 | 0.3151 | |
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| 0.368 | 2.7 | 2160 | 0.3093 | |
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| 0.368 | 3.0 | 2400 | 0.3070 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
