Training complete

README.md

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+---
+license: apache-2.0
+base_model: google/mt5-small
+tags:
+- summarization
+- generated_from_trainer
+datasets:
+- xlsum
+metrics:
+- rouge
+model-index:
+- name: mt5-small-finetuned-summary
+  results:
+  - task:
+      name: Sequence-to-sequence Language Modeling
+      type: text2text-generation
+    dataset:
+      name: xlsum
+      type: xlsum
+      config: french
+      split: validation
+      args: french
+    metrics:
+    - name: Rouge1
+      type: rouge
+      value: 22.1761
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# mt5-small-finetuned-summary
+
+This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the xlsum dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.2770
+- Rouge1: 22.1761
+- Rouge2: 8.0115
+- Rougel: 18.8245
+- Rougelsum: 18.8574
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5.6e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 8
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum |
+|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
+| 4.7862        | 1.0   | 1085 | 2.5085          | 21.2935 | 7.4006 | 18.0791 | 18.0868   |
+| 3.2212        | 2.0   | 2170 | 2.4069          | 21.968  | 7.9139 | 18.5785 | 18.6248   |
+| 3.0201        | 3.0   | 3255 | 2.3489          | 21.8529 | 7.9865 | 18.6842 | 18.7074   |
+| 2.9085        | 4.0   | 4340 | 2.3173          | 22.1605 | 8.2646 | 18.8284 | 18.838    |
+| 2.8285        | 5.0   | 5425 | 2.2965          | 22.0612 | 8.0447 | 18.7454 | 18.7784   |
+| 2.7727        | 6.0   | 6510 | 2.2899          | 22.1416 | 7.9747 | 18.7622 | 18.8029   |
+| 2.7311        | 7.0   | 7595 | 2.2797          | 22.2979 | 8.1382 | 18.9798 | 19.035    |
+| 2.7171        | 8.0   | 8680 | 2.2770          | 22.1761 | 8.0115 | 18.8245 | 18.8574   |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.15.0
+- Tokenizers 0.15.0

config.json

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+{
+  "_name_or_path": "google/mt5-small",
+  "architectures": [
+    "MT5ForConditionalGeneration"
+  ],
+  "classifier_dropout": 0.0,
+  "d_ff": 1024,
+  "d_kv": 64,
+  "d_model": 512,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "gelu_new",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "mt5",
+  "num_decoder_layers": 8,
+  "num_heads": 6,
+  "num_layers": 8,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "tokenizer_class": "T5Tokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.2",
+  "use_cache": true,
+  "vocab_size": 250112
+}

generation_config.json

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+{
+  "decoder_start_token_id": 0,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.35.2"
+}

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special_tokens_map.json

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tokenizer_config.json

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+}

training_args.bin

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