Create README.md

README.md

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+---
+language:
+- ru
+- ru-RU
+tags:
+- summarization
+- t5
+datasets:
+- IlyaGusev/gazeta
+license: apache-2.0
+---
+
+# RuT5SumGazeta
+
+## Model description
+
+This is the model for abstractive summarization for Russian based on [rut5-base](https://huggingface.co/cointegrated/rut5-base).
+
+
+## Intended uses & limitations
+
+#### How to use
+
+```python
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+article_text = "..."
+
+model_name = "IlyaGusev/rut5-base-sum-gazeta"
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+
+input_ids = tokenizer(
+    [article_text],
+    add_special_tokens=True,
+    padding="max_length",
+    truncation=True,
+    max_length=400,
+    return_tensors="pt"
+)["input_ids"]
+
+output_ids = model.generate(
+    input_ids=input_ids,
+    max_length=200,
+    no_repeat_ngram_size=3,
+    num_beams=5,
+    early_stopping=True
+)[0]
+
+summary = tokenizer.decode(output_ids, skip_special_tokens=True)
+print(summary)
+```
+
+## Training data
+
+- Dataset: https://github.com/IlyaGusev/gazeta
+
+## Training procedure
+
+- Training script: [TBA]
+
+## Eval results
+
+| Model                     | R-1-f | R-2-f | R-L-f | chrF | BLEU |
+|:--------------------------|:------|:------|:------|:-----|:-----|
+| rut5-base-sum-gazeta      | 32.3  | 14.5  | 27.9  | 39.6 | 11.5 |
+
+Predicting all summaries:
+```python
+import json
+import torch
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+from datasets import load_dataset
+
+
+def gen_batch(inputs, batch_size):
+    batch_start = 0
+    while batch_start < len(inputs):
+        yield inputs[batch_start: batch_start + batch_size]
+        batch_start += batch_size
+
+
+def predict(
+    model_name,
+    input_records,
+    output_file,
+    max_source_tokens_count=400,
+    max_target_tokens_count=200,
+    batch_size=16
+):
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    tokenizer = MBartTokenizer.from_pretrained(model_name)
+    model = MBartForConditionalGeneration.from_pretrained(model_name).to(device)
+    
+    predictions = []
+    for batch in gen_batch(input_records, batch_size):
+        texts = [r["text"] for r in batch]
+        input_ids = tokenizer(
+            texts,                                                                                                     
+            add_special_tokens=True,
+            max_length=max_source_tokens_count,
+            padding="max_length",
+            truncation=True,
+            return_tensors="pt"
+        )["input_ids"].to(device)
+        
+        output_ids = model.generate(
+            input_ids=input_ids,
+            max_length=max_target_tokens_count,
+            no_repeat_ngram_size=3,
+            num_beams=5,
+            early_stopping=True
+        )
+        summaries = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+        for s in summaries:
+            print(s)
+        predictions.extend(summaries)
+    with open(output_file, "w") as w:
+        for p in predictions:
+            w.write(p.strip().replace("\n", " ") + "\n")
+
+gazeta_test = load_dataset('IlyaGusev/gazeta', script_version="v1.0")["test"]
+predict("IlyaGusev/mbart_ru_sum_gazeta", gazeta_test["test"], "t5_predictions.txt")
+```
+
+Evaluation: https://github.com/IlyaGusev/summarus/blob/master/evaluate.py
+
+Flags: --language ru --tokenize-after --lower