Update README.md

README.md

@@ -37,7 +37,7 @@ datasets:
 - ArielUW/jobtitles
 ---
 
-# model_output
+# 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:
@@ -65,46 +65,46 @@ To use this model, you will need to install the transformers and sentencepiece l
   !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.'}] 
+    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))
+    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.
+  - 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.
+  - 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).