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---
library_name: transformers
base_model: MiMe-MeMo/MeMo-BERT-03
tags:
- generated_from_trainer
model-index:
- name: memobert3_ED1
results: []
---
<!-- 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](https://huggingface.co/MiMe-MeMo/MeMo-BERT-03) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6065
- F1-score: 0.9098
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1-score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 69 | 0.3808 | 0.8439 |
| No log | 2.0 | 138 | 0.4234 | 0.8432 |
| No log | 3.0 | 207 | 0.4577 | 0.9013 |
| No log | 4.0 | 276 | 0.6278 | 0.8851 |
| No log | 5.0 | 345 | 0.6449 | 0.8517 |
| No log | 6.0 | 414 | 0.7495 | 0.8678 |
| No log | 7.0 | 483 | 0.6065 | 0.9098 |
| 0.1663 | 8.0 | 552 | 0.6217 | 0.9098 |
| 0.1663 | 9.0 | 621 | 0.6420 | 0.9098 |
| 0.1663 | 10.0 | 690 | 0.6514 | 0.9098 |
| 0.1663 | 11.0 | 759 | 0.6627 | 0.9098 |
| 0.1663 | 12.0 | 828 | 0.6726 | 0.9098 |
| 0.1663 | 13.0 | 897 | 0.6828 | 0.9016 |
| 0.1663 | 14.0 | 966 | 0.6904 | 0.9016 |
| 0.0001 | 15.0 | 1035 | 0.6942 | 0.9016 |
| 0.0001 | 16.0 | 1104 | 0.6976 | 0.9016 |
| 0.0001 | 17.0 | 1173 | 0.7007 | 0.9016 |
| 0.0001 | 18.0 | 1242 | 0.7027 | 0.9016 |
| 0.0001 | 19.0 | 1311 | 0.7038 | 0.9016 |
| 0.0001 | 20.0 | 1380 | 0.7037 | 0.9016 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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