End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: muchad/idt5-base
+tags:
+- generated_from_trainer
+metrics:
+- rouge
+- bleu
+model-index:
+- name: idt5-base-qaqg_v3
+  results: []
+---
+
+<!-- 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. -->
+
+# idt5-base-qaqg_v3
+
+This model is a fine-tuned version of [muchad/idt5-base](https://huggingface.co/muchad/idt5-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.3062
+- Rouge1: 0.3601
+- Rouge2: 0.1916
+- Rougel: 0.3400
+- Rougelsum: 0.3400
+- Bleu: 0.1048
+
+## 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: 0.0001
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu   |
+|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:------:|
+| 1.5911        | 1.0   | 4695  | 1.4095          | 0.3387 | 0.1717 | 0.3191 | 0.3191    | 0.0946 |
+| 1.3763        | 2.0   | 9390  | 1.3437          | 0.3500 | 0.1839 | 0.3305 | 0.3304    | 0.1004 |
+| 1.2634        | 3.0   | 14085 | 1.3158          | 0.3576 | 0.1892 | 0.3385 | 0.3385    | 0.1024 |
+| 1.1552        | 4.0   | 18780 | 1.2977          | 0.3589 | 0.1889 | 0.3390 | 0.3391    | 0.1010 |
+| 1.1018        | 5.0   | 23475 | 1.3062          | 0.3601 | 0.1916 | 0.3400 | 0.3400    | 0.1048 |
+
+
+### Framework versions
+
+- Transformers 4.46.0
+- Pytorch 2.4.0a0+f70bd71a48.nv24.06
+- Datasets 3.0.2
+- Tokenizers 0.20.1

config.json

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+{
+  "_name_or_path": "muchad/idt5-base",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "classifier_dropout": 0.0,
+  "d_ff": 2048,
+  "d_kv": 64,
+  "d_model": 768,
+  "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": "t5",
+  "num_decoder_layers": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "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": "4.46.0",
+  "use_cache": true,
+  "vocab_size": 30000
+}

generation_config.json

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+{
+  "decoder_start_token_id": 0,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.46.0"
+}

model.safetensors

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+size 977270632

special_tokens_map.json

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+{
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "sep_token": "[SEP]",
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

spiece.model

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tokenizer_config.json

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+{
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
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+      "lstrip": false,
+      "normalized": false,
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+      "special": true
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+  "additional_special_tokens": [],
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "extra_ids": 0,
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "sep_token": "[SEP]",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "T5Tokenizer",
+  "unk_token": "<unk>"
+}

training_args.bin

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