metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - cartesinus/iva_mt_wslot
metrics:
  - bleu
model-index:
  - name: iva_mt_wslot-m2m100_418M-en-fr
    results:
      - task:
          name: Sequence-to-sequence Language Modeling
          type: text2text-generation
        dataset:
          name: iva_mt_wslot
          type: iva_mt_wslot
          config: en-fr
          split: validation
          args: en-fr
        metrics:
          - name: Bleu
            type: bleu
            value: 72.5602
language:
  - en
  - fr
pipeline_tag: translation

iva_mt_wslot-m2m100_418M-en-fr

This model is a fine-tuned version of facebook/m2m100_418M on the iva_mt_wslot dataset. It achieves the following results on the evaluation set:

Loss: 0.0094
Bleu: 72.5602
Gen Len: 21.9543

Model description

More information needed

How to use

First please make sure to install pip install transformers. First download model:

from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
import torch

def translate(input_text, lang):
    input_ids = tokenizer(input_text, return_tensors="pt")
    generated_tokens = model.generate(**input_ids, forced_bos_token_id=tokenizer.get_lang_id(lang))
    return tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)

model_name = "cartesinus/iva_mt_wslot-m2m100_418M-0.1.0-en-fr"
tokenizer = M2M100Tokenizer.from_pretrained(model_name, src_lang="en", tgt_lang="fr")
model = M2M100ForConditionalGeneration.from_pretrained(model_name)

Then you can translate either plain text like this:

print(translate("set the temperature on my thermostat", "fr"))

or you can translate with slot annotations that will be restored in tgt language:

print(translate("wake me up at <a>nine am<a> on <b>friday<b>", "fr"))

Limitations of translation with slot transfer:

Annotated words must be placed between semi-xml tags like this "this is <a>example<a>"
There is no closing tag for example "<\a>" in the above example - this is done on purpose to omit problems with backslash escape
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"
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

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: 7
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Gen Len
0.0132	1.0	1700	0.0110	68.7161	21.6874
0.0083	2.0	3400	0.0093	70.3712	21.9443
0.006	3.0	5100	0.0093	71.5485	21.995
0.0044	4.0	6800	0.0091	71.2971	21.8371
0.0032	5.0	8500	0.0093	71.9252	21.9268
0.0026	6.0	10200	0.0094	72.2756	21.9543
0.002	7.0	11900	0.0094	72.5602	21.9543

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.3