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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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import torch |
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tokenizer = AutoTokenizer.from_pretrained("piazzola/test1") |
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model = AutoModelForSeq2SeqLM.from_pretrained("piazzola/test1") |
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sentence = "i left the keys in the car." |
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with torch.no_grad(): |
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inputs = tokenizer([sentence], return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=100) |
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generated_text = tokenizer.decode(outputs[0]) |
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print(generated_text) |
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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: 0.1609 |
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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: 4 |
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- eval_batch_size: 4 |
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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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| 0.3088 | 0.3 | 7458 | 0.2634 | |
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| 0.2615 | 0.6 | 14916 | 0.2143 | |
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| 0.2294 | 0.9 | 22374 | 0.1951 | |
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| 0.2137 | 1.2 | 29832 | 0.1830 | |
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| 0.1944 | 1.5 | 37290 | 0.1736 | |
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| 0.1918 | 1.8 | 44748 | 0.1682 | |
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| 0.18 | 2.1 | 52206 | 0.1659 | |
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| 0.1801 | 2.4 | 59664 | 0.1623 | |
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| 0.185 | 2.7 | 67122 | 0.1609 | |
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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 |
