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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+mrebel-large - bnb 4bits
+- Model creator: https://huggingface.co/Babelscape/
+- Original model: https://huggingface.co/Babelscape/mrebel-large/
+
+
+
+
+Original model description:
+---
+language:
+- ar
+- ca
+- de
+- el
+- en
+- es
+- fr
+- hi
+- it
+- ja
+- ko
+- nl
+- pl
+- pt
+- ru
+- sv
+- vi
+- zh
+widget:
+- text: >-
+    Els Red Hot Chili Peppers es van formar a Los Angeles per Kiedis, Flea, el
+    guitarrista Hillel Slovak i el bateria Jack Irons.
+  example_title: Catalan
+inference:
+  parameters:
+    decoder_start_token_id: 250058
+    src_lang: ca_XX
+    tgt_lang: <triplet>
+tags:
+- seq2seq
+- relation-extraction
+license: cc-by-nc-sa-4.0
+pipeline_tag: translation
+datasets:
+- Babelscape/SREDFM
+---
+# RED<sup>FM</sup>: a Filtered and Multilingual Relation Extraction Dataset
+
+This is 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://arxiv.org/abs/2306.09802). 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://arxiv.org/abs/2306.09802). If you use the code or model, please reference this work in your paper:
+
+    @inproceedings{huguet-cabot-et-al-2023-redfm-dataset,
+        title = "RED$^{\rm FM}$: a Filtered and Multilingual Relation Extraction Dataset",
+        author = "Huguet Cabot, Pere-Llu{\'\i}s  and Tedeschi, Simone and Ngonga Ngomo, Axel-Cyrille and
+          Navigli, Roberto",
+        booktitle = "Proc. of the 61st Annual Meeting of the Association for Computational Linguistics: ACL 2023",
+        month = jul,
+        year = "2023",
+        address = "Toronto, Canada",
+        publisher = "Association for Computational Linguistics",
+        url = "https://arxiv.org/abs/2306.09802",
+    }
+
+The original repository for the paper can be found [here](https://github.com/Babelscape/rebel#REDFM)
+
+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 mREBEL and its pre-training dataset check the [Spaces demo](https://huggingface.co/spaces/Babelscape/mrebel-demo).
+
+## Pipeline usage
+
+```python
+from transformers import pipeline
+
+triplet_extractor = pipeline('translation_xx_to_yy', 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.", decoder_start_token_id=250058, src_lang="en_XX", tgt_lang="<triplet>", return_tensors=True, return_text=False)[0]["translation_token_ids"]]) # change en_XX for the language of the source.
+print(extracted_text[0])
+# Function to parse the generated text and extract the triplets
+def extract_triplets_typed(text):
+    triplets = []
+    relation = ''
+    text = text.strip()
+    current = 'x'
+    subject, relation, object_, object_type, subject_type = '','','','',''
+
+    for token in text.replace("<s>", "").replace("<pad>", "").replace("</s>", "").replace("tp_XX", "").replace("__en__", "").split():
+        if token == "<triplet>" or token == "<relation>":
+            current = 't'
+            if relation != '':
+                triplets.append({'head': subject.strip(), 'head_type': subject_type, 'type': relation.strip(),'tail': object_.strip(), 'tail_type': object_type})
+                relation = ''
+            subject = ''
+        elif token.startswith("<") and token.endswith(">"):
+            if current == 't' or current == 'o':
+                current = 's'
+                if relation != '':
+                    triplets.append({'head': subject.strip(), 'head_type': subject_type, 'type': relation.strip(),'tail': object_.strip(), 'tail_type': object_type})
+                object_ = ''
+                subject_type = token[1:-1]
+            else:
+                current = 'o'
+                object_type = token[1:-1]
+                relation = ''
+        else:
+            if current == 't':
+                subject += ' ' + token
+            elif current == 's':
+                object_ += ' ' + token
+            elif current == 'o':
+                relation += ' ' + token
+    if subject != '' and relation != '' and object_ != '' and object_type != '' and subject_type != '':
+        triplets.append({'head': subject.strip(), 'head_type': subject_type, 'type': relation.strip(),'tail': object_.strip(), 'tail_type': object_type})
+    return triplets
+extracted_triplets = extract_triplets_typed(extracted_text[0])
+print(extracted_triplets)
+```
+
+## Model and Tokenizer using transformers
+
+```python
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+def extract_triplets_typed(text):
+    triplets = []
+    relation = ''
+    text = text.strip()
+    current = 'x'
+    subject, relation, object_, object_type, subject_type = '','','','',''
+
+    for token in text.replace("<s>", "").replace("<pad>", "").replace("</s>", "").replace("tp_XX", "").replace("__en__", "").split():
+        if token == "<triplet>" or token == "<relation>":
+            current = 't'
+            if relation != '':
+                triplets.append({'head': subject.strip(), 'head_type': subject_type, 'type': relation.strip(),'tail': object_.strip(), 'tail_type': object_type})
+                relation = ''
+            subject = ''
+        elif token.startswith("<") and token.endswith(">"):
+            if current == 't' or current == 'o':
+                current = 's'
+                if relation != '':
+                    triplets.append({'head': subject.strip(), 'head_type': subject_type, 'type': relation.strip(),'tail': object_.strip(), 'tail_type': object_type})
+                object_ = ''
+                subject_type = token[1:-1]
+            else:
+                current = 'o'
+                object_type = token[1:-1]
+                relation = ''
+        else:
+            if current == 't':
+                subject += ' ' + token
+            elif current == 's':
+                object_ += ' ' + token
+            elif current == 'o':
+                relation += ' ' + token
+    if subject != '' and relation != '' and object_ != '' and object_type != '' and subject_type != '':
+        triplets.append({'head': subject.strip(), 'head_type': subject_type, 'type': relation.strip(),'tail': object_.strip(), 'tail_type': object_type})
+    return triplets
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("Babelscape/mrebel-large", src_lang="en_XX", tgt_lang="tp_XX") 
+# Here we set English ("en_XX") as source language. To change the source language swap the first token of the input for your desired language or change to supported language. For catalan ("ca_XX") or greek ("el_EL") (not included in mBART pretraining) you need a workaround:
+# tokenizer._src_lang = "ca_XX"
+# tokenizer.cur_lang_code_id = tokenizer.convert_tokens_to_ids("ca_XX")
+# tokenizer.set_src_lang_special_tokens("ca_XX")
+model = AutoModelForSeq2SeqLM.from_pretrained("Babelscape/mrebel-large")
+gen_kwargs = {
+    "max_length": 256,
+    "length_penalty": 0,
+    "num_beams": 3,
+    "num_return_sequences": 3,
+    "forced_bos_token_id": None,
+}
+
+# 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),
+    decoder_start_token_id = tokenizer.convert_tokens_to_ids("tp_XX"),
+    **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_typed(sentence))
+```
+
+## License
+
+This model is licensed under the CC BY-SA 4.0 license. The text of the license can be found [here](https://creativecommons.org/licenses/by-nc-sa/4.0/).
+