Update README.md

README.md

@@ -1,3 +1,160 @@
 ---
-license: mit
+language:
+- 'no'
+- nb
+- nn
+- en
+inference: false
+tags:
+- Norwegian
+- English
+- translation
+license: cc-by-4.0
+pipeline_tag: translation
 ---
+
+# NorT5 base finetuned for English ↔ Norwegian (Bokmål or Nynorsk, all 6 directions) translation
+
+<img src="https://huggingface.co/ltg/norbert3-base/resolve/main/norbert.png" width=12.5%>
+
+
+
+## Example usage
+
+This model is specifically finetuned for translating documents in any direction between Norwegian Bokmål, Norwegian Nynorsk and English.
+Unlike traditional NMT models, it is trained on paragraph-to-paragraph translation – the translation quality is thus better if you feed it whole paragraphs instead of segmented sentences.
+
+A simple example of how to use this model can be found in the `translate.py` file:
+
+```python
+import torch
+import transformers
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from transformers.generation import LogitsProcessor
+
+
+class RepetitionPenaltyLogitsProcessor(LogitsProcessor):
+    def __init__(self, penalty: float, model):
+        last_bias = model.classifier.nonlinearity[-1].bias.data
+        last_bias = torch.nn.functional.log_softmax(last_bias)
+        self.penalty = penalty * (last_bias - last_bias.max())
+
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
+        penalized_score = torch.gather(scores + self.penalty.unsqueeze(0).to(input_ids.device), 1, input_ids).to(scores.dtype)
+        scores.scatter_(1, input_ids, penalized_score)
+        return scores
+
+
+class Translator:
+    def __init__(self, model_path="ltg/nort5-base-en-no-translation", device="cpu"):
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
+        self.cls_index = self.tokenizer.convert_tokens_to_ids("[CLS]")
+        self.sep_index = self.tokenizer.convert_tokens_to_ids("[SEP]")
+        self.eos_index = self.tokenizer.convert_tokens_to_ids("[EOS]")
+        self.pad_index = self.tokenizer.convert_tokens_to_ids("[PAD]")
+        self.eng_index = self.tokenizer.convert_tokens_to_ids(">>eng<<")
+        self.nob_index = self.tokenizer.convert_tokens_to_ids(">>nob<<")
+        self.nno_index = self.tokenizer.convert_tokens_to_ids(">>nno<<")
+
+        self.model = AutoModelForSeq2SeqLM.from_pretrained(model_path, trust_remote_code=True)
+
+        self.device = device
+        print(f"SYSTEM: Running on {self.device}", flush=True)
+
+        self.model = self.model.to(device)
+        self.model.eval()
+
+        print(f"Sucessfully loaded the model to the memory")
+
+        self.LANGUAGE_IDS = {
+            "en": self.eng_index,
+            "nb": self.nob_index,
+            "nn": self.nno_index
+        }
+
+    def __call__(self, source, source_language, target_language):
+        source = [s.strip() for s in source.split('\n')]
+        source_subwords = self.tokenizer(source).input_ids
+        source_subwords = [[self.cls_index, self.LANGUAGE_IDS[target_language], self.LANGUAGE_IDS[source_language]] + s + [self.sep_index] for s in source_subwords]
+        source_subwords = [torch.tensor(s) for s in source_subwords]
+        source_subwords = torch.nn.utils.rnn.pad_sequence(source_subwords, batch_first=True, padding_value=self.pad_index)
+        source_subwords = source_subwords[:, :512].to(self.device)
+
+        def generate(model, **kwargs):
+            with torch.inference_mode():
+                with torch.autocast(enabled=self.device != "cpu", device_type="cuda", dtype=torch.bfloat16):
+                    return model.generate(**kwargs)
+
+        generate_kwargs = dict(
+            input_ids=source_subwords,
+            attention_mask=(source_subwords != self.pad_index).long(),
+            max_new_tokens = 512-1,
+            num_beams=8,
+            length_penalty=1.6,
+            early_stopping=True,
+            do_sample=False,
+            use_cache=True,
+            logits_processor=[RepetitionPenaltyLogitsProcessor(0.5, self.model), transformers.LogitNormalization()]
+        )
+        output = generate(self.model, **generate_kwargs).tolist()
+        paragraphs = [self.tokenizer.decode(c, skip_special_tokens=True).strip() for c in output]
+        translation = '\n'.join(paragraphs)
+
+        return translation
+
+
+if __name__ == "__main__":
+
+    translator = Translator()
+
+    en_text = "How are you feeling right now? Better?"
+    no_text = translator(en_text, "en", "nb")
+
+    print(en_text)
+    print(no_text)
+```
+
+
+## The NorT5 and NorBERT family
+
+The official release of a new generation of NorT5 language models described in paper [**NorBench — A Benchmark for Norwegian Language Models**](https://arxiv.org/abs/2305.03880). Plese read the paper to learn more details about the model.
+
+
+## Other sizes:
+- [NorT5 xs (32M)](https://huggingface.co/ltg/nort5-xs)
+- [NorT5 small (88M)](https://huggingface.co/ltg/nort5-small)
+- [NorT5 base (228M)](https://huggingface.co/ltg/nort5-base)
+- [NorT5 large (808M)](https://huggingface.co/ltg/nort5-large)
+
+
+## Encoder-only NorBERT siblings:
+- [NorBERT 3 xs (15M)](https://huggingface.co/ltg/norbert3-xs)
+- [NorBERT 3 small (40M)](https://huggingface.co/ltg/norbert3-small)
+- [NorBERT 3 base (123M)](https://huggingface.co/ltg/norbert3-base)
+- [NorBERT 3 large (323M)](https://huggingface.co/ltg/norbert3-large)
+
+
+## Cite us
+
+```bibtex
+@inproceedings{samuel-etal-2023-norbench,
+    title = "{N}or{B}ench {--} A Benchmark for {N}orwegian Language Models",
+    author = "Samuel, David  and
+      Kutuzov, Andrey  and
+      Touileb, Samia  and
+      Velldal, Erik  and
+      {\O}vrelid, Lilja  and
+      R{\o}nningstad, Egil  and
+      Sigdel, Elina  and
+      Palatkina, Anna",
+    booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
+    month = may,
+    year = "2023",
+    address = "T{\'o}rshavn, Faroe Islands",
+    publisher = "University of Tartu Library",
+    url = "https://aclanthology.org/2023.nodalida-1.61",
+    pages = "618--633",
+    abstract = "We present NorBench: a streamlined suite of NLP tasks and probes for evaluating Norwegian language models (LMs) on standardized data splits and evaluation metrics. We also introduce a range of new Norwegian language models (both encoder and encoder-decoder based). Finally, we compare and analyze their performance, along with other existing LMs, across the different benchmark tests of NorBench.",
+}
+
+```