Create README.md

README.md

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+---
+language:
+- ar
+- ca
+- de
+- el
+- en
+- es
+- fr
+- hi
+- it
+- ja
+- ko
+- nl
+- pl
+- pt
+- ru
+- sv
+- vi
+- zh
+widget:
+- text: >-
+    The Red Hot Chili Peppers were formed in Los Angeles by Kiedis, Flea, guitarist Hillel Slovak and drummer Jack Irons.
+tags:
+- seq2seq
+- relation-extraction
+
+license: cc-by-nc-sa-4.0
+---
+# RED^{FM}: a Filtered and Multilingual Relation Extraction Dataset
+
+This a multilingual version of [REBEL](https://huggingface.co/Babelscape/rebel-large). It can be used as a standalone multulingual Relation Extraction system, or as a pretrained system to be tuned on multilingual Relation Extraction datasets.
+
+mREBEL is introduced in the ACL 2023 paper [RED^{FM}: a Filtered and Multilingual Relation Extraction Dataset](https://github.com/Babelscape/rebel/blob/main/docs/). We present a new multilingual Relation Extraction dataset and train a multilingual version of REBEL which reframed Relation Extraction as a seq2seq task. The paper can be found [here](https://github.com/Babelscape/rebel/blob/main/docs/). If you use the code or model, please reference this work in your paper:
+
+    @inproceedings{huguet-cabot-et-al-2023-red,
+        title = "RED^{FM}: a Filtered and Multilingual Relation Extraction Dataset",
+        author = "Huguet Cabot, Pere-Llu{\'\i}s  and
+          Navigli, Roberto",
+        booktitle = "ACL 2023",
+        month = jul,
+        year = "2023",
+        address = "Toronto, Canada",
+        publisher = "Association for Computational Linguistics",
+    }
+
+The original repository for the paper can be found [here](https://github.com/Babelscape/rebel)
+
+Be aware that the inference widget at the right does not output special tokens, which are necessary to distinguish the subject, object and relation types. For a demo of REBEL and its pre-training dataset check the [Spaces demo](https://huggingface.co/spaces/Babelscape/rebel-demo).
+
+## Pipeline usage
+
+```python
+from transformers import pipeline
+
+triplet_extractor = pipeline('text2text-generation', model='Babelscape/mrebel-large', tokenizer='Babelscape/mrebel-large')
+# We need to use the tokenizer manually since we need special tokens.
+extracted_text = triplet_extractor.tokenizer.batch_decode([triplet_extractor("The Red Hot Chili Peppers were formed in Los Angeles by Kiedis, Flea, guitarist Hillel Slovak and drummer Jack Irons.", return_tensors=True, return_text=False)[0]["generated_token_ids"]])
+print(extracted_text[0])
+# Function to parse the generated text and extract the triplets
+def extract_triplets(text):
+    triplets = []
+    relation, subject, relation, object_ = '', '', '', ''
+    text = text.strip()
+    current = 'x'
+    for token in text.replace("<s>", "").replace("<pad>", "").replace("</s>", "").split():
+        if token == "<triplet>":
+            current = 't'
+            if relation != '':
+                triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})
+                relation = ''
+            subject = ''
+        elif token == "<subj>":
+            current = 's'
+            if relation != '':
+                triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})
+            object_ = ''
+        elif token == "<obj>":
+            current = 'o'
+            relation = ''
+        else:
+            if current == 't':
+                subject += ' ' + token
+            elif current == 's':
+                object_ += ' ' + token
+            elif current == 'o':
+                relation += ' ' + token
+    if subject != '' and relation != '' and object_ != '':
+        triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})
+    return triplets
+extracted_triplets = extract_triplets(extracted_text[0])
+print(extracted_triplets)
+```
+
+## Model and Tokenizer using transformers
+
+```python
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+def extract_triplets(text):
+    triplets = []
+    relation, subject, relation, object_ = '', '', '', ''
+    text = text.strip()
+    current = 'x'
+    for token in text.replace("<s>", "").replace("<pad>", "").replace("</s>", "").split():
+        if token == "<triplet>":
+            current = 't'
+            if relation != '':
+                triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})
+                relation = ''
+            subject = ''
+        elif token == "<subj>":
+            current = 's'
+            if relation != '':
+                triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})
+            object_ = ''
+        elif token == "<obj>":
+            current = 'o'
+            relation = ''
+        else:
+            if current == 't':
+                subject += ' ' + token
+            elif current == 's':
+                object_ += ' ' + token
+            elif current == 'o':
+                relation += ' ' + token
+    if subject != '' and relation != '' and object_ != '':
+        triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})
+    return triplets
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("Babelscape/rebel-large")
+model = AutoModelForSeq2SeqLM.from_pretrained("Babelscape/rebel-large")
+gen_kwargs = {
+    "max_length": 256,
+    "length_penalty": 0,
+    "num_beams": 3,
+    "num_return_sequences": 3,
+}
+
+# Text to extract triplets from
+text = 'The Red Hot Chili Peppers were formed in Los Angeles by Kiedis, Flea, guitarist Hillel Slovak and drummer Jack Irons.'
+
+# Tokenizer text
+model_inputs = tokenizer(text, max_length=256, padding=True, truncation=True, return_tensors = 'pt')
+
+# Generate
+generated_tokens = model.generate(
+    model_inputs["input_ids"].to(model.device),
+    attention_mask=model_inputs["attention_mask"].to(model.device),
+    **gen_kwargs,
+)
+
+# Extract text
+decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=False)
+
+# Extract triplets
+for idx, sentence in enumerate(decoded_preds):
+    print(f'Prediction triplets sentence {idx}')
+    print(extract_triplets(sentence))
+```