Translation
Transformers
PyTorch
Safetensors
m2m_100
text2text-generation
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("kazandaev/m2m100_1.2B")
model = AutoModelForSeq2SeqLM.from_pretrained("kazandaev/m2m100_1.2B")Quick Links
m2m100_1.2B
This model is a fine-tuned version of facebook/m2m100_1.2B on the wmt16 dataset. It achieves the following results on the evaluation set:
- Loss: 0.8189
- Bleu: 33.3632
- Gen Len: 36.176
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: 1
- seed: 42
- gradient_accumulation_steps: 10
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| 0.7163 | 1.0 | 47790 | 0.8189 | 33.3632 | 36.176 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.14.1
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Model tree for kazandaev/m2m100_1.2B
Base model
facebook/m2m100_1.2BDataset used to train kazandaev/m2m100_1.2B
Evaluation results
- Bleu on wmt16validation set self-reported33.363
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="kazandaev/m2m100_1.2B")