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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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metrics: |
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- bleu |
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model-index: |
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- name: iva_mt_wslot-m2m100_418M-en-sv |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: iva_mt_wslot |
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type: iva_mt_wslot |
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config: en-sv |
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split: validation |
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args: en-sv |
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metrics: |
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- name: Bleu |
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type: bleu |
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value: 71.0808 |
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datasets: |
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- cartesinus/iva_mt_wslot |
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language: |
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- en |
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- sv |
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pipeline_tag: translation |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# iva_mt_wslot-m2m100_418M-en-sv |
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This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the iva_mt_wslot dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0107 |
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- Bleu: 71.0808 |
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- Gen Len: 19.7647 |
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## Model description |
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More information needed |
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## How to use |
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First please make sure to install `pip install transformers`. First download model: |
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```python |
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from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer |
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import torch |
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def translate(input_text, lang): |
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input_ids = tokenizer(input_text, return_tensors="pt") |
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generated_tokens = model.generate(**input_ids, forced_bos_token_id=tokenizer.get_lang_id(lang)) |
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return tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) |
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model_name = "cartesinus/iva_mt_wslot-m2m100_418M-0.1.0-en-sv" |
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tokenizer = M2M100Tokenizer.from_pretrained(model_name, src_lang="en", tgt_lang="sv") |
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model = M2M100ForConditionalGeneration.from_pretrained(model_name) |
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``` |
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Then you can translate either plain text like this: |
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```python |
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print(translate("set the temperature on my thermostat", "sv")) |
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``` |
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or you can translate with slot annotations that will be restored in tgt language: |
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```python |
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print(translate("wake me up at <a>nine am<a> on <b>friday<b>", "sv")) |
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``` |
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Limitations of translation with slot transfer: |
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1) Annotated words must be placed between semi-xml tags like this "this is \<a\>example\<a\>" |
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2) There is no closing tag for example "\<\a\>" in above example - this is done on purpose to omit problems with backslash escape |
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3) If the sentence consists of more than one slot then simply use the next alphabet letter. For example "this is \<a\>example\<a\> with more than \<b\>one\<b\> slot" |
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4) Please do not add space before the first or last annotated word because this particular model was trained this way and it most probably will lower its results |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 7 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |
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|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| |
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| 0.0151 | 1.0 | 1885 | 0.0120 | 67.2332 | 19.3956 | |
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| 0.0095 | 2.0 | 3770 | 0.0105 | 69.8147 | 19.675 | |
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| 0.0065 | 3.0 | 5655 | 0.0104 | 70.239 | 19.8404 | |
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| 0.0049 | 4.0 | 7540 | 0.0104 | 70.3673 | 19.7154 | |
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| 0.0038 | 5.0 | 9425 | 0.0105 | 70.1632 | 19.7743 | |
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| 0.0026 | 6.0 | 11310 | 0.0105 | 70.7959 | 19.7809 | |
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| 0.0021 | 7.0 | 13195 | 0.0107 | 71.0808 | 19.7647 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
