File size: 3,318 Bytes
a9d25f6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
---
library_name: transformers
base_model: MiMe-MeMo/MeMo-BERT-03
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: memo3_indirect_speech
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. -->
# memo3_indirect_speech
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:
- Accuracy: 0.6840
- Precision: 0.7036
- Recall: 0.6840
- F1: 0.6798
- Loss: 0.7225
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Accuracy | Precision | Recall | F1 | Validation Loss |
|:-------------:|:-------:|:----:|:--------:|:---------:|:------:|:------:|:---------------:|
| No log | 1.0 | 9 | 0.3820 | 0.1459 | 0.3820 | 0.2112 | 1.3936 |
| No log | 2.0 | 18 | 0.3820 | 0.1459 | 0.3820 | 0.2112 | 1.5289 |
| No log | 3.0 | 27 | 0.5385 | 0.2900 | 0.5385 | 0.3770 | 1.0207 |
| No log | 4.0 | 36 | 0.3820 | 0.1459 | 0.3820 | 0.2112 | 1.3341 |
| No log | 5.0 | 45 | 0.5385 | 0.2900 | 0.5385 | 0.3770 | 1.0219 |
| No log | 6.0 | 54 | 0.3829 | 0.1825 | 0.3829 | 0.2130 | 1.0394 |
| No log | 7.0 | 63 | 0.6020 | 0.5949 | 0.6020 | 0.5650 | 0.8312 |
| No log | 8.0 | 72 | 0.6250 | 0.6270 | 0.6250 | 0.5846 | 0.8301 |
| No log | 9.0 | 81 | 0.6821 | 0.7037 | 0.6821 | 0.6491 | 0.7462 |
| No log | 10.0 | 90 | 0.5620 | 0.6878 | 0.5620 | 0.5268 | 0.8255 |
| No log | 11.0 | 99 | 0.5968 | 0.6945 | 0.5968 | 0.5751 | 0.7890 |
| No log | 12.0 | 108 | 0.6901 | 0.6877 | 0.6901 | 0.6868 | 0.7190 |
| No log | 13.0 | 117 | 0.6020 | 0.7003 | 0.6020 | 0.5805 | 0.8426 |
| No log | 14.0 | 126 | 0.6932 | 0.6919 | 0.6932 | 0.6899 | 0.7213 |
| No log | 15.0 | 135 | 0.7158 | 0.7300 | 0.7158 | 0.6928 | 0.7416 |
| No log | 16.0 | 144 | 0.6503 | 0.7054 | 0.6503 | 0.6417 | 0.7546 |
| No log | 17.0 | 153 | 0.7031 | 0.7104 | 0.7031 | 0.6993 | 0.6824 |
| No log | 17.8235 | 160 | 0.6840 | 0.7036 | 0.6840 | 0.6798 | 0.7225 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Tokenizers 0.21.0
|