Instructions to use Shakhovak/flan-t5-small-absa-rest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shakhovak/flan-t5-small-absa-rest with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Shakhovak/flan-t5-small-absa-rest") model = AutoModelForSeq2SeqLM.from_pretrained("Shakhovak/flan-t5-small-absa-rest", device_map="auto") - Notebooks
- Google Colab
- Kaggle
flan-t5-small-absa-rest
This model is a fine-tuned version of google/flan-t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2865
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.6057 | 0.52 | 100 | 1.0232 |
| 1.0344 | 1.05 | 200 | 0.4973 |
| 0.7178 | 1.57 | 300 | 0.4004 |
| 0.619 | 2.09 | 400 | 0.3607 |
| 0.5466 | 2.62 | 500 | 0.3492 |
| 0.5277 | 3.14 | 600 | 0.3245 |
| 0.4775 | 3.66 | 700 | 0.3175 |
| 0.4823 | 4.19 | 800 | 0.3090 |
| 0.4406 | 4.71 | 900 | 0.3035 |
| 0.454 | 5.24 | 1000 | 0.2995 |
| 0.4108 | 5.76 | 1100 | 0.2960 |
| 0.4101 | 6.28 | 1200 | 0.2940 |
| 0.4016 | 6.81 | 1300 | 0.2901 |
| 0.3888 | 7.33 | 1400 | 0.2868 |
| 0.3815 | 7.85 | 1500 | 0.2865 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.0.1+cu118
- Datasets 2.18.0
- Tokenizers 0.15.2
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Base model
google/flan-t5-small