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README.md

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 ---
+language: ru
 license: apache-2.0
+datasets:
+- IlyaGusev/gazeta
 ---
+
+# RuT5LargeSumGazeta
+
+## Model description
+
+This is the model for abstractive summarization for Russian based on ai-forever/ruT5-large.
+
+## Intended uses & limitations
+
+### How to use
+
+Here is how to use this model in PyTorch:
+
+```python
+from transformers import AutoTokenizer, T5ForConditionalGeneration
+
+model_name = "mlenjoyneer/rut5_large_sum_gazeta"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+
+article_text = "..."
+
+input_ids = tokenizer(
+    [article_text],
+    max_length=600,
+    add_special_tokens=True,
+    padding="max_length",
+    truncation=True,
+    return_tensors="pt"
+)["input_ids"]
+
+output_ids = model.generate(
+    input_ids=input_ids,
+    no_repeat_ngram_size=4
+)[0]
+
+summary = tokenizer.decode(output_ids, skip_special_tokens=True)
+print(summary)
+```
+
+## Training data
+
+- Dataset: [Gazeta](https://huggingface.co/datasets/IlyaGusev/gazeta)
+
+## Evaluation results
+
+| Model                                        | R-1-f | R-2-f | R-L-f | chrF | BLEU | Avg char length |
+| -------------------------------------------- | ----- | ----- | ----- | ---- | ---- | --------------- |
+| IlyaGusev/mbart_ru_sum_gazeta                | 28.7  | 11.1  | 24.4  | **37.3** | **9.4**  | 373     |
+| IlyaGusev/rut5_base_sum_gazeta               | 28.6  | 11.1  | 24.5  | 37.2 | **9.4**  | 331         |
+| IlyaGusev/rugpt3medium_sum_gazeta            | 24.1  | 6.5   | 19.8  | 32.1 | 3.6  | 242             |
+| rut5-large_sum_gazeta                        | **29.6**  | **11.7**  | **25.2**  | **37.3** | **9.4**  | 304 |

config.json

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+{
+  "_name_or_path": "./rut5-large",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "bos_token_id": 1,
+  "d_ff": 4096,
+  "d_kv": 64,
+  "d_model": 1024,
+  "decoder_start_token_id": 1,
+  "dense_act_fn": "relu",
+  "dropout_rate": 0.1,
+  "eos_token_id": 2,
+  "feed_forward_proj": "relu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": false,
+  "layer_norm_epsilon": 1e-06,
+  "max_length": 120,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_beams": 4,
+  "num_decoder_layers": 24,
+  "num_heads": 16,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.31.0",
+  "use_cache": true,
+  "vocab_size": 32128
+}

generation_config.json

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+{
+  "bos_token_id": 1,
+  "decoder_start_token_id": 1,
+  "eos_token_id": 2,
+  "max_length": 120,
+  "num_beams": 4,
+  "pad_token_id": 0,
+  "transformers_version": "4.31.0"
+}

old_spiece.model

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pytorch_model.bin

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spiece.model

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