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xlmrlarge-squad2-webis
intanm/xlmrlarge-squad2-webis
xlmrlarge-squad2-webis is a question answering model from intanm. Use it when the input is a question plus a passage. It is set up for transformers. The card lists the license as cc-by-4.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Question Answering
- Library
- transformers
- License
- cc-by-4.0
- Model type
- xlm-roberta
- Access
- Public
- Created
- May 19, 2023
- Updated
- Oct 22, 2023
- SHA
- f3d856d3
Try a prompt
Datasets
Model card
From the Hugging Face model README
<!-- 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. -->
20230519-001-baseline-xlmrl-qa-squadv2-ft-clickbait-spoiling
This model is a fine-tuned version of deepset/xlm-roberta-large-squad2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 6.2849
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 200 | 2.2379 |
| No log | 2.0 | 400 | 2.3140 |
| 1.8489 | 3.0 | 600 | 3.0345 |
| 1.8489 | 4.0 | 800 | 3.5503 |
| 0.5002 | 5.0 | 1000 | 3.9071 |
| 0.5002 | 6.0 | 1200 | 4.4492 |
| 0.5002 | 7.0 | 1400 | 5.3196 |
| 0.158 | 8.0 | 1600 | 5.7806 |
| 0.158 | 9.0 | 1800 | 6.0289 |
| 0.0542 | 10.0 | 2000 | 6.2849 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
- Task
- Question Answering
- Library
- transformers
- Type
- xlm-roberta
- License
- cc-by-4.0
- Created
- May 19, 2023
- Updated
- Oct 22, 2023