---
library_name: transformers
license: mit
base_model: facebook/m2m100_1.2B
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: neutral_job_title_rephraser_pl
  results:
  - task:
      type: text2text-generation
      name: Gender-Neutral Job Title Rephrasing
    dataset:
      type: ArielUW/jobtitles
      name: Job Titles Dataset
      config: default
      split: test
    metrics:
    - type: bleu
      value: 93.9441
      name: BLEU
    - type: precision  
      value: 1.0
      name: Attempted Noun Neutralisation Precision
    - type: recall
      value: 0.892
      name: Attempted Noun Neutralisation Recall
    - type: levenshtein  
      value: 0.0395
      name: Normalized Levenshtein Distance (neutralization needed)
    - type: levenshtein  
      value: 0.0001
      name: Normalized Levenshtein Distance (neutralization not needed)
datasets:
- ArielUW/jobtitles
---

# neutral_job_title_rephraser_pl

This model is a fine-tuned version of [facebook/m2m100_1.2B](https://huggingface.co/facebook/m2m100_1.2B) on [ArielUW/jobtitles](https://huggingface.co/datasets/ArielUW/jobtitles) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7263
- Bleu: 93.9441
- Gen Len: 36.358

## Model description

The aim of this model is to provide gender-neutral terms for job titles in Polish in single sentences. The optimal outcome looks like this:<br>
  *Jestem pracownikiem tej firmy.*<br>
  turns into<br>
  *Jestem osobą pracowniczą tej firmy.*<br>
Sentences not containing such terms are not expected to change at all, for example:<br>
  *Mam uroczego kotka.*<br>
  turns into<br>
  *Mam uroczego kotka.*<br>

In terms of actual outcomes and errors in outputs, see our [readme](https://github.com/ArielUW/IMLLA-FinalProject/blob/main/README.md).

# Model usage

To use this model, you will need to install the transformers and sentencepiece libraries:

    !pip install transformers sentencepiece

You can then use the model directly through the pipeline API, which provides a high-level interface for text generation:

    from transformers import pipeline
    pipe = pipeline("text2text-generation", model="mongrz/model_output")
    gender_neutral_text = pipe("Pielęgniarki protestują pod sejmem.")
    print(gender_neutral_text)
    #expected output: [{'generated_text': 'Osoby pielęgniarskie protestują pod sejmem.'}] 

This will create a pipeline object for text-to-text generation using your model. You can then pass the input text to the pipe object to generate the gender-neutral version. The output will be a list of dictionaries, each containing the generated text.
Alternatively, you can still load the tokenizer and model manually for more fine-grained control:
    
    from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
    
    tokenizer = AutoTokenizer.from_pretrained("mongrz/model_output")
    model = AutoModelForSeq2SeqLM.from_pretrained("mongrz/model_output")
    
    text_to_translate = "Pielęgniarki protestują pod sejmem." 
    model_inputs = tokenizer(text_to_translate, return_tensors="pt")
    
    #Generate gender-neutral text
    gen_tokens = model.generate(**model_inputs, forced_bos_token_id=tokenizer.get_lang_id("pl"))
    
    #Decode and print the generated text 
    print(tokenizer.batch_decode(gen_tokens, skip_special_tokens=True))
    
This approach allows you to access the tokenizer and model directly and customize the generation process further if needed. Choose the method that best suits your needs.
  
## Intended uses & limitations
While this model demonstrates promising results in generating gender-neutral job titles in Polish, it has certain limitations:

  - Low-Frequency Items: The model may struggle with less common job titles or words that were not frequently present in the training data. It might produce inaccurate or unexpected outputs for such cases.
  - Morphosyntactically Complex Cases: Items requiring rare or non-typical patterns of forming personatives can pose challenges for the model. The accuracy of the generated output may decrease in such scenarios.
  - Feminine Nouns: The model has shown to sometimes underperform when dealing with feminine nouns, potentially due to biases or patterns in the training data. Further investigation and fine-tuning are needed to address this limitation.
  - Single Sentence Input: The model is optimized for single-sentence inputs and might not produce the desired results for single-word items, longer texts or paragraphs. It might fail to maintain context, coherence and terminological consistency across multiple sentences. Its performance for single-word items has not been tested.
  - Domain Specificity: The model is trained on a specific dataset of single sentences with job titles and without them. It may not generalize well to other domains or contexts. It might need further fine-tuning to adapt to different types of text or specific vocabulary.

    More information regarding issues, errors and limitations, see our [readme](https://github.com/ArielUW/IMLLA-FinalProject/blob/main/README.md).
## Training and evaluation data

This model was evaluated using several metrics to assess its performance:

  - BLEU (Bilingual Evaluation Understudy): BLEU is a widely used metric for evaluating machine translation quality. It measures the overlap between the generated text and the reference text in terms of n-grams. A higher BLEU score indicates better translation quality. The model achieved a BLEU score of 93.9441 on the evaluation set, indicating high accuracy in generating gender-neutral terms.
  - Attempted Noun Neutralisation Precision: This metric measures the proportion of correctly attempted neutralizations (i.e., items that required neutralization, not necessarily correctly formed neutral items) out of all attempted neutralizations. The model achieved a precision of 1, indicating that all attempted neutralizations were performed on items that required it.
  - Attempted Noun Neutralisation Recall: This metric measures the proportion of nouns that had a neutralization attempt present in the generated text out of all nouns that should have been neutralized. The model achieved a recall of 0.892, suggesting that it successfully recognized items requiring neutralization in the majority of cases.
  - Normalized Levenshtein's Distance: This metric calculates the edit distance between the generated text and the reference text, normalized by the length of the reference text. It provides a measure of similarity between the two texts. The model achieved a Levenshtein's distance of 0.0395 for sentences requiring neutralization and 0.0001 for the items that should not have been changed at all, indicating a high degree of similarity between the generated text and the reference text.

More information on the evaluation outcomes can be found in [our readme](https://github.com/ArielUW/IMLLA-FinalProject/blob/main/README.md).

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAFACTOR and the args are:
No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 7
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Bleu    | Gen Len |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|
| 23.051        | 1.0    | 38   | 4.3445          | 89.5045 | 35.746  |
| 15.9099       | 2.0    | 76   | 3.5044          | 91.9617 | 36.366  |
| 12.7846       | 3.0    | 114  | 2.8211          | 92.7676 | 36.22   |
| 10.3083       | 4.0    | 152  | 2.3006          | 93.675  | 36.284  |
| 8.4622        | 5.0    | 190  | 1.9316          | 93.6498 | 36.348  |
| 7.3015        | 6.0    | 228  | 1.7263          | 93.9441 | 36.358  |
| 6.8211        | 6.8212 | 259  | 1.6685          | 93.7274 | 36.306  |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0