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--- |
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datasets: |
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- rebel-short |
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metrics: |
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- rouge |
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model-index: |
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- name: flan-t5-base |
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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: rebel-short |
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type: rebel-short |
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config: default |
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split: test |
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args: default |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 51.5716 |
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license: cc-by-sa-4.0 |
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language: |
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- nl |
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pipeline_tag: text2text-generation |
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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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# flan-rebel-nl |
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This model is a fine-tuned version of flan-t5-base on the rebel-short dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1029 |
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- Rouge1: 51.5716 |
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- Rouge2: 40.2152 |
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- Rougel: 49.9941 |
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- Rougelsum: 49.9767 |
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- Gen Len: 18.5898 |
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## Model description |
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This is a flan-t5-base model fine-tuned on a Dutch dataset version based on RBEL: Relation Extraction By End-to-end Language generation. The model aims to extract triplets in the form {head, relation, tail} from unstructured text. The data for Dutch triplets and unstructured text was generated by using the code of the original authors of REBEL, available at https://github.com/Babelscape/crocodile. |
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## Pipeline usage |
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The code below is adopted from the original REBEL model: https://huggingface.co/Babelscape/rebel-large . |
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```python |
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from transformers import pipeline |
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triplet_extractor = pipeline('text2text-generation', model='Kbrek/flan_rebel_nl', tokenizer='Kbrek/flan_rebel_nl') |
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# We need to use the tokenizer manually since we need special tokens. |
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extracted_text = triplet_extractor("Nederland is een van de landen binnen het Koninkrijk der Nederlanden. Nederland ligt voor het overgrote deel in het noordwesten van Europa, aan de Noordzee. ", max_length = 512, num_beams = 3, temperature = 1) |
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# Function to parse the generated text and extract the triplets |
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def extract_triplets(text): |
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triplets = [] |
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relation, subject, relation, object_ = '', '', '', '' |
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text = text.strip() |
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current = 'x' |
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for token in text.replace("<s>", "").replace("<pad>", "").replace("</s>", "").split(): |
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if token == "<triplet>": |
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current = 't' |
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if relation != '': |
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triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()}) |
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relation = '' |
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subject = '' |
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elif token == "<subj>": |
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current = 's' |
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if relation != '': |
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triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()}) |
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object_ = '' |
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elif token == "<obj>": |
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current = 'o' |
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relation = '' |
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else: |
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if current == 't': |
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subject += ' ' + token |
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elif current == 's': |
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object_ += ' ' + token |
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elif current == 'o': |
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relation += ' ' + token |
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if subject != '' and relation != '' and object_ != '': |
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triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()}) |
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return triplets |
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extracted_triplets = extract_triplets(extracted_text[0]) |
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print(extracted_triplets) |
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``` |
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A trick that might give you better results is by forcing the entities the model generates by extracting entities with a ner pipeline and forcing those tokens in the generated output. |
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```python |
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triplet_extractor = pipeline('text2text-generation', model='Kbrek/flan_rebel_nl', tokenizer='Kbrek/flan_rebel_nl') |
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ner_extractor = pipeline("ner", "Babelscape/wikineural-multilingual-ner", aggregation_strategy = "simple") |
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#extract ents |
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ner_output = ner_extractor(input_text) |
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ents = [i["word"] for i in ner_output] |
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if len(ents) > 0: |
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tokens = triplet_extractor.tokenizer(ents, add_special_tokens=False)["input_ids"] |
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extracted_text = triplet_extractor(input_text, max_length = 512, force_words_ids = tokens) |
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else: |
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extracted_text = triplet_extractor(input_text, max_length = 512, temperature = 1) |
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triplets = extract_triplets(extracted_text[0]["generated_text"]) |
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``` |
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## Training and evaluation data |
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Data used for developing and evaluating this model is generated by using https://github.com/Babelscape/crocodile . |
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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: 5e-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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
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| 0.1256 | 1.0 | 22047 | 0.1206 | 50.3892 | 38.2761 | 48.7657 | 48.7444 | 18.6112 | |
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| 0.1091 | 2.0 | 44094 | 0.1112 | 50.9615 | 39.2843 | 49.3865 | 49.3674 | 18.5447 | |
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| 0.0875 | 3.0 | 66141 | 0.1047 | 51.2045 | 39.7598 | 49.6483 | 49.6317 | 18.5763 | |
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| 0.0841 | 4.0 | 88188 | 0.1036 | 51.3543 | 39.9776 | 49.8528 | 49.8223 | 18.6178 | |
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| 0.0806 | 5.0 | 110235 | 0.1029 | 51.5716 | 40.2152 | 49.9941 | 49.9767 | 18.5898 | |
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### Framework versions |
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- Transformers 4.27.2 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.10.1 |
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- Tokenizers 0.12.1 |
