End of training

README.md

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+---
+license: apache-2.0
+base_model: google/mt5-base
+tags:
+- generated_from_trainer
+metrics:
+- rouge
+model-index:
+- name: mT5-TextSimp-LT-BatchSize12-lr5e-5
+  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. -->
+
+# mT5-TextSimp-LT-BatchSize12-lr5e-5
+
+This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: nan
+- Rouge1: 0.0
+- Rouge2: 0.0
+- Rougel: 0.0
+- Gen Len: 0.0
+
+## 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: 5e-05
+- train_batch_size: 12
+- eval_batch_size: 12
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 8
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Gen Len |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:-------:|
+| 28.0514       | 1.43  | 200  | 21.4337         | 0.0028 | 0.0    | 0.0028 | 512.0   |
+| 0.0           | 2.86  | 400  | nan             | 0.0    | 0.0    | 0.0    | 0.0     |
+| 0.0           | 4.29  | 600  | nan             | 0.0    | 0.0    | 0.0    | 0.0     |
+| 0.0           | 5.71  | 800  | nan             | 0.0    | 0.0    | 0.0    | 0.0     |
+| 0.0           | 7.14  | 1000 | nan             | 0.0    | 0.0    | 0.0    | 0.0     |
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 2.1.1
+- Datasets 2.16.1
+- Tokenizers 0.15.0

config.json

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+{
+  "_name_or_path": "google/mt5-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": "4.36.2",
+  "use_cache": true,
+  "vocab_size": 250112
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "decoder_start_token_id": 0,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.36.2"
+}

logs/events.out.tfevents.1712141782.ESBeastModeOn.25264.0

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model.safetensors

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special_tokens_map.json

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+}

spiece.model

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tokenizer_config.json

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+  "sp_model_kwargs": {},
+  "tokenizer_class": "T5Tokenizer",
+  "unk_token": "<unk>"
+}

training_args.bin

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