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memobert3_ED1
yemen2016/memobert3_ED1
memobert3_ED1 is a text classification model from yemen2016. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- xlm-roberta
- Access
- Public
- Created
- Sep 11, 2024
- Updated
- Sep 11, 2024
- SHA
- 79db3e4e
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Base models
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. -->
memobert3_ED1
This model is a fine-tuned version of MiMe-MeMo/MeMo-BERT-03 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2698
- F1-score: 0.9344
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: 8
- eval_batch_size: 8
- 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 | F1-score |
|---|---|---|---|---|
| No log | 1.0 | 69 | 0.2789 | 0.8934 |
| No log | 2.0 | 138 | 0.3454 | 0.8839 |
| No log | 3.0 | 207 | 0.2698 | 0.9344 |
| No log | 4.0 | 276 | 0.4248 | 0.9262 |
| No log | 5.0 | 345 | 0.5399 | 0.9097 |
| No log | 6.0 | 414 | 0.5981 | 0.8933 |
| No log | 7.0 | 483 | 0.6430 | 0.9013 |
| 0.1618 | 8.0 | 552 | 0.6388 | 0.9096 |
| 0.1618 | 9.0 | 621 | 0.6361 | 0.9096 |
| 0.1618 | 10.0 | 690 | 0.6451 | 0.9096 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- Created
- Sep 11, 2024
- Updated
- Sep 11, 2024