End of training

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,115 @@
+---
+license: apache-2.0
+base_model: google/mt5-small
+tags:
+- generated_from_trainer
+metrics:
+- rouge
+model-index:
+- name: mt5-summarize-te
+  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-summarize-te
+
+This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.1257
+- Rouge1: 0.5211
+- Rouge2: 0.4338
+- Rougel: 0.4813
+- Rougelsum: 0.4819
+
+## 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: 0.0005
+- train_batch_size: 2
+- eval_batch_size: 1
+- seed: 42
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 90
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
+| 3.8667        | 0.2   | 100  | 2.5990          | 0.4450 | 0.3695 | 0.4151 | 0.4154    |
+| 3.2326        | 0.39  | 200  | 2.5107          | 0.5023 | 0.4156 | 0.4567 | 0.4571    |
+| 3.1169        | 0.59  | 300  | 2.4503          | 0.5092 | 0.4204 | 0.4775 | 0.4762    |
+| 2.9083        | 0.79  | 400  | 2.4005          | 0.5053 | 0.4179 | 0.4699 | 0.4709    |
+| 2.9652        | 0.98  | 500  | 2.3218          | 0.4833 | 0.4003 | 0.4528 | 0.4555    |
+| 2.8848        | 1.18  | 600  | 2.3262          | 0.5309 | 0.4415 | 0.4868 | 0.4879    |
+| 2.6585        | 1.37  | 700  | 2.3118          | 0.5168 | 0.4273 | 0.4780 | 0.4773    |
+| 2.6662        | 1.57  | 800  | 2.2823          | 0.5112 | 0.4233 | 0.4713 | 0.4727    |
+| 2.7628        | 1.77  | 900  | 2.2381          | 0.5158 | 0.4269 | 0.4798 | 0.4798    |
+| 2.7156        | 1.96  | 1000 | 2.2466          | 0.5280 | 0.4452 | 0.4836 | 0.4844    |
+| 2.5683        | 2.16  | 1100 | 2.2495          | 0.5184 | 0.4300 | 0.4779 | 0.4773    |
+| 2.5248        | 2.36  | 1200 | 2.2498          | 0.5179 | 0.4282 | 0.4790 | 0.4803    |
+| 2.5809        | 2.55  | 1300 | 2.2336          | 0.5233 | 0.4385 | 0.4895 | 0.4920    |
+| 2.7113        | 2.75  | 1400 | 2.2368          | 0.5079 | 0.4207 | 0.4707 | 0.4716    |
+| 2.6151        | 2.95  | 1500 | 2.1993          | 0.5108 | 0.4236 | 0.4681 | 0.4679    |
+| 2.5172        | 3.14  | 1600 | 2.2197          | 0.5138 | 0.4257 | 0.4778 | 0.4781    |
+| 2.5873        | 3.34  | 1700 | 2.1900          | 0.5185 | 0.4312 | 0.4823 | 0.4821    |
+| 2.4245        | 3.53  | 1800 | 2.1982          | 0.5222 | 0.4332 | 0.4837 | 0.4853    |
+| 2.4983        | 3.73  | 1900 | 2.1756          | 0.5125 | 0.4247 | 0.4809 | 0.4810    |
+| 2.3963        | 3.93  | 2000 | 2.1900          | 0.5259 | 0.4400 | 0.4870 | 0.4884    |
+| 2.3465        | 4.12  | 2100 | 2.1963          | 0.5300 | 0.4412 | 0.4900 | 0.4915    |
+| 2.4625        | 4.32  | 2200 | 2.1818          | 0.5277 | 0.4384 | 0.4868 | 0.4882    |
+| 2.4257        | 4.52  | 2300 | 2.1504          | 0.5212 | 0.4342 | 0.4833 | 0.4842    |
+| 2.368         | 4.71  | 2400 | 2.1463          | 0.5252 | 0.4418 | 0.4856 | 0.4869    |
+| 2.427         | 4.91  | 2500 | 2.1581          | 0.5161 | 0.4267 | 0.4766 | 0.4771    |
+| 2.3443        | 5.11  | 2600 | 2.1551          | 0.5167 | 0.4281 | 0.4794 | 0.4794    |
+| 2.2923        | 5.3   | 2700 | 2.1596          | 0.5183 | 0.4255 | 0.4668 | 0.4686    |
+| 2.2956        | 5.5   | 2800 | 2.1438          | 0.5125 | 0.4268 | 0.4747 | 0.4754    |
+| 2.2973        | 5.69  | 2900 | 2.1523          | 0.5139 | 0.4259 | 0.4712 | 0.4722    |
+| 2.3013        | 5.89  | 3000 | 2.1514          | 0.5138 | 0.4236 | 0.4741 | 0.4742    |
+| 2.2222        | 6.09  | 3100 | 2.1558          | 0.5172 | 0.4300 | 0.4773 | 0.4784    |
+| 2.3957        | 6.28  | 3200 | 2.1451          | 0.5203 | 0.4326 | 0.4815 | 0.4817    |
+| 2.1995        | 6.48  | 3300 | 2.1476          | 0.5146 | 0.4264 | 0.4747 | 0.4752    |
+| 2.2931        | 6.68  | 3400 | 2.1252          | 0.5120 | 0.4252 | 0.4683 | 0.4683    |
+| 2.3062        | 6.87  | 3500 | 2.1313          | 0.5197 | 0.4339 | 0.4803 | 0.4807    |
+| 2.2844        | 7.07  | 3600 | 2.1281          | 0.5197 | 0.4339 | 0.4868 | 0.4876    |
+| 2.1158        | 7.27  | 3700 | 2.1438          | 0.5208 | 0.4333 | 0.4818 | 0.4823    |
+| 2.2523        | 7.46  | 3800 | 2.1221          | 0.5197 | 0.4324 | 0.4783 | 0.4788    |
+| 2.2389        | 7.66  | 3900 | 2.1336          | 0.5144 | 0.4262 | 0.4769 | 0.4771    |
+| 2.2209        | 7.85  | 4000 | 2.1317          | 0.5211 | 0.4338 | 0.4813 | 0.4819    |
+| 2.1828        | 8.05  | 4100 | 2.1366          | 0.5208 | 0.4336 | 0.4814 | 0.4816    |
+| 2.2746        | 8.25  | 4200 | 2.1325          | 0.5219 | 0.4342 | 0.4819 | 0.4823    |
+| 2.229         | 8.44  | 4300 | 2.1334          | 0.5214 | 0.4329 | 0.4809 | 0.4812    |
+| 2.2762        | 8.64  | 4400 | 2.1223          | 0.5161 | 0.4288 | 0.4761 | 0.4769    |
+| 2.2005        | 8.84  | 4500 | 2.1322          | 0.5197 | 0.4320 | 0.4793 | 0.4799    |
+| 2.1975        | 9.03  | 4600 | 2.1294          | 0.5211 | 0.4338 | 0.4813 | 0.4819    |
+| 2.3219        | 9.23  | 4700 | 2.1251          | 0.5148 | 0.4260 | 0.4768 | 0.4772    |
+| 2.252         | 9.43  | 4800 | 2.1261          | 0.5211 | 0.4338 | 0.4813 | 0.4819    |
+| 2.2594        | 9.62  | 4900 | 2.1236          | 0.5200 | 0.4331 | 0.4808 | 0.4814    |
+| 2.1675        | 9.82  | 5000 | 2.1257          | 0.5211 | 0.4338 | 0.4813 | 0.4819    |
+
+
+### Framework versions
+
+- Transformers 4.35.0
+- Pytorch 2.1.0+cu118
+- Datasets 2.14.6
+- Tokenizers 0.14.1

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,
+  "length_penalty": 0.6,
+  "max_length": 128,
+  "model_type": "mt5",
+  "no_repeat_ngram_size": 2,
+  "num_beams": 15,
+  "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": "4.35.0",
+  "use_cache": true,
+  "vocab_size": 250112
+}

generation_config.json

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+  "max_length": 128,
+  "no_repeat_ngram_size": 2,
+  "num_beams": 15,
+  "pad_token_id": 0,
+  "transformers_version": "4.35.0"
+}

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tokenizer_config.json

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