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---
license: mit
tags:
- generated_from_trainer
datasets:
- iva_mt_wslot
metrics:
- bleu
model-index:
- name: iva_mt_wslot-m2m100_418M-en-pl-plaintext_10e
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: iva_mt_wslot
type: iva_mt_wslot
config: en-pl
split: validation
args: en-pl
metrics:
- name: Bleu
type: bleu
value: 41.3124
---
<!-- 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. -->
# iva_mt_wslot-m2m100_418M-en-pl-plaintext_10e
This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the iva_mt_wslot dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0169
- Bleu: 41.3124
- Gen Len: 15.5197
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.0169 | 1.0 | 5091 | 0.0162 | 36.663 | 15.6444 |
| 0.0124 | 2.0 | 10182 | 0.0151 | 38.36 | 15.6314 |
| 0.0086 | 3.0 | 15273 | 0.0150 | 39.3808 | 15.5507 |
| 0.0069 | 4.0 | 20364 | 0.0152 | 39.6307 | 15.5235 |
| 0.0049 | 5.0 | 25455 | 0.0156 | 40.4441 | 15.5911 |
| 0.0038 | 6.0 | 30546 | 0.0159 | 40.3781 | 15.47 |
| 0.0027 | 7.0 | 35637 | 0.0163 | 40.1339 | 15.4722 |
| 0.0021 | 8.0 | 40728 | 0.0166 | 41.4429 | 15.4906 |
| 0.0016 | 9.0 | 45819 | 0.0168 | 41.1024 | 15.5249 |
| 0.0012 | 10.0 | 50910 | 0.0169 | 41.3124 | 15.5197 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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