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--- |
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license: cc-by-4.0 |
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datasets: |
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- AnnaWegmann/Dialog-Paraphrase |
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language: |
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- en |
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base_model: microsoft/deberta-v3-large |
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--- |
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Model was created as described in https://arxiv.org/abs/2404.06670 , this is the best `DeBERTa AGGREGATED` model. See also the [GitHub](https://github.com/nlpsoc/Paraphrases-in-News-Interviews) repository. |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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import torch |
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class ParaphraseHighlighter: |
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def __init__(self, model_name="AnnaWegmann/Highlight-Paraphrases-in-Dialog"): |
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# Load the tokenizer and model |
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self.tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True) |
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self.model = AutoModelForTokenClassification.from_pretrained(model_name) |
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# Get the label id for 'LABEL_1' |
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self.label2id = self.model.config.label2id |
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self.label_id = self.label2id['LABEL_1'] |
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def highlight_paraphrase(self, text1, text2): |
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# Tokenize the inputs with the tokenizer |
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encoding = self.tokenizer(text1, text2, return_tensors="pt", padding=True, truncation=True) |
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outputs = self.model(**encoding) |
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logits = outputs.logits # Shape: (batch_size, sequence_length, num_labels) |
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# Apply softmax to get probabilities, automatically places [SEP] token |
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probs = torch.nn.functional.softmax(logits, dim=-1) # Shape: (batch_size, sequence_length, num_labels) |
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# Convert token IDs back to tokens |
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tokens = self.tokenizer.convert_ids_to_tokens(encoding["input_ids"][0]) |
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# Get word IDs to map tokens to words |
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word_ids = encoding.word_ids(batch_index=0) |
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# Get sequence IDs to know which text the token belongs to |
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sequence_ids = encoding.sequence_ids(batch_index=0) |
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# Collect words and probabilities for each text |
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words_text1 = [] |
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words_text2 = [] |
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probs_text1 = [] |
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probs_text2 = [] |
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previous_word_idx = None |
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# For determining if there are high-probability words in both texts |
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has_high_prob_text1 = False |
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has_high_prob_text2 = False |
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for idx, (word_idx, seq_id) in enumerate(zip(word_ids, sequence_ids)): |
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if word_idx is None: |
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# Skip special tokens like [CLS], [SEP], [PAD] |
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continue |
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if word_idx != previous_word_idx: |
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# Start of a new word |
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word_tokens = [tokens[idx]] |
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# Get the probability for LABEL_1 for the first token of the word |
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prob_LABEL_1 = probs[0][idx][self.label_id].item() |
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# Collect subsequent tokens belonging to the same word |
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j = idx + 1 |
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while j < len(word_ids) and word_ids[j] == word_idx: |
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word_tokens.append(tokens[j]) |
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j += 1 |
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# Reconstruct the word |
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word = self.tokenizer.convert_tokens_to_string(word_tokens).strip() |
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# Check if probability >= 0.5 to uppercase |
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if prob_LABEL_1 >= 0.5: |
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word_display = word.upper() |
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if seq_id == 0: |
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has_high_prob_text1 = True |
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elif seq_id == 1: |
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has_high_prob_text2 = True |
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else: |
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word_display = word |
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# Append the word and probability to the appropriate list |
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if seq_id == 0: |
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words_text1.append(word_display) |
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probs_text1.append(prob_LABEL_1) |
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elif seq_id == 1: |
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words_text2.append(word_display) |
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probs_text2.append(prob_LABEL_1) |
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else: |
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# Should not happen |
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pass |
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previous_word_idx = word_idx |
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# Determine if there are words in both texts with prob >= 0.5 |
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if has_high_prob_text1 and has_high_prob_text2: |
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print("is a paraphrase") |
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else: |
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print("is not a paraphrase") |
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# Function to format and align words and probabilities |
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def print_aligned(words, probs): |
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# Determine the maximum length of words for formatting |
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max_word_length = max(len(word) for word in words) |
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# Create format string for alignment |
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format_str = f'{{:<{max_word_length}}}' |
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# Print words |
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for word in words: |
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print(format_str.format(word), end=' ') |
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print() |
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# Print probabilities aligned below words |
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for prob in probs: |
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prob_str = f"{prob:.2f}" |
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print(format_str.format(prob_str), end=' ') |
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print('\n') |
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# Print text1's words and probabilities aligned |
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print("\nSpeaker 1:") |
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print_aligned(words_text1, probs_text1) |
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# Print text2's words and probabilities aligned |
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print("Speaker 2:") |
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print_aligned(words_text2, probs_text2) |
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# Example usage |
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highlighter = ParaphraseHighlighter() |
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text1 = "And it will be my 20th time in doing it as a television commentator from Rome so." |
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text2 = "Yes, you've been doing this for a while now." |
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highlighter.highlight_paraphrase(text1, text2) |
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``` |
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should return |
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``` |
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is a paraphrase |
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Speaker 1: |
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And IT will BE MY 20TH TIME IN DOING IT as a TELEVISION COMMENTATOR from Rome so. |
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0.15 0.54 0.49 0.56 0.74 0.83 0.77 0.75 0.78 0.76 0.44 0.45 0.52 0.52 0.30 0.37 0.21 |
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Speaker 2: |
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Yes, YOU'VE BEEN DOING THIS FOR A WHILE NOW. |
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0.12 0.79 0.78 0.82 0.82 0.69 0.70 0.72 0.66 |
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``` |
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For comments or questions reach out to Anna (a.m.wegmann @ uu.nl) or raise an issue on GitHub. |
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If you find this model helpful, consider citing our paper: |
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``` |
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@article{wegmann2024, |
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title={What's Mine becomes Yours: Defining, Annotating and Detecting Context-Dependent Paraphrases in News Interview Dialogs}, |
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author={Wegmann, Anna and Broek, Tijs van den and Nguyen, Dong}, |
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journal={arXiv preprint arXiv:2404.06670}, |
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year={2024} |
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} |
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``` |
