File size: 2,793 Bytes
04bb66c |
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 87 88 |
---
license: mit
tags:
- generated_from_trainer
datasets:
- un_multi
metrics:
- bleu
model-index:
- name: m2m100_418M-evaluated-en-to-ar-2000instancesUNMULTI-leaningRate2e-05-batchSize8-regu2
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: un_multi
type: un_multi
args: ar-en
metrics:
- name: Bleu
type: bleu
value: 40.8245
---
<!-- 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. -->
# m2m100_418M-evaluated-en-to-ar-2000instancesUNMULTI-leaningRate2e-05-batchSize8-regu2
This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the un_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3642
- Bleu: 40.8245
- Meteor: 0.4272
- Gen Len: 41.8075
## 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: 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: 11
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|
| 5.1584 | 0.5 | 100 | 3.2518 | 30.3723 | 0.3633 | 41.5 |
| 2.1351 | 1.0 | 200 | 0.9929 | 32.9915 | 0.3833 | 41.8225 |
| 0.568 | 1.5 | 300 | 0.4312 | 33.705 | 0.3896 | 42.6225 |
| 0.3749 | 2.0 | 400 | 0.3697 | 36.9316 | 0.4084 | 40.57 |
| 0.2376 | 2.5 | 500 | 0.3587 | 37.6782 | 0.4124 | 41.99 |
| 0.2435 | 3.0 | 600 | 0.3529 | 37.9931 | 0.4128 | 42.02 |
| 0.1706 | 3.5 | 700 | 0.3531 | 39.9972 | 0.4252 | 41.8025 |
| 0.165 | 4.0 | 800 | 0.3514 | 39.3155 | 0.42 | 41.0275 |
| 0.1273 | 4.5 | 900 | 0.3606 | 40.0765 | 0.4234 | 41.6175 |
| 0.1307 | 5.0 | 1000 | 0.3550 | 40.4468 | 0.428 | 41.72 |
| 0.0926 | 5.5 | 1100 | 0.3603 | 40.5454 | 0.4307 | 41.765 |
| 0.1096 | 6.0 | 1200 | 0.3613 | 40.5691 | 0.4298 | 42.31 |
| 0.0826 | 6.5 | 1300 | 0.3642 | 40.8245 | 0.4272 | 41.8075 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|