Instructions to use gmguarino/climateguard_claim_extraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gmguarino/climateguard_claim_extraction with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("gmguarino/climateguard_claim_extraction") model = AutoModelForSeq2SeqLM.from_pretrained("gmguarino/climateguard_claim_extraction", device_map="auto") - Notebooks
- Google Colab
- Kaggle
climateguard_claim_extraction
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: 2.4622
- Rouge1: 0.1155
- Rouge2: 0.0169
- Rougel: 0.0909
- Rougelsum: 0.0905
- Gen Len: 20.0
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 | 14 | 2.5620 | 0.1 | 0.0084 | 0.0792 | 0.0795 | 20.0 |
| No log | 2.0 | 28 | 2.4958 | 0.1208 | 0.0183 | 0.0933 | 0.0931 | 20.0 |
| No log | 3.0 | 42 | 2.4703 | 0.1237 | 0.022 | 0.0983 | 0.0982 | 20.0 |
| No log | 4.0 | 56 | 2.4622 | 0.1155 | 0.0169 | 0.0909 | 0.0905 | 20.0 |
Framework versions
- Transformers 4.57.0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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Model tree for gmguarino/climateguard_claim_extraction
Base model
google-t5/t5-small