End of training

README.md

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+---
+license: apache-2.0
+base_model: Malmika/T5-Small-Sinhala-Sumarization
+tags:
+- generated_from_trainer
+metrics:
+- rouge
+model-index:
+- name: T5-Small-Sinhala-Sumarization-test3
+  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. -->
+
+# T5-Small-Sinhala-Sumarization-test3
+
+This model is a fine-tuned version of [Malmika/T5-Small-Sinhala-Sumarization](https://huggingface.co/Malmika/T5-Small-Sinhala-Sumarization) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0304
+- Rouge1: 0.1355
+- Rouge2: 0.0618
+- Rougel: 0.1354
+- Rougelsum: 0.1356
+- Gen Len: 17.8198
+
+## 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: 2e-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: 5
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
+|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
+| 0.0959        | 1.0   | 4333  | 0.0560          | 0.1357 | 0.062  | 0.1357 | 0.1358    | 17.8575 |
+| 0.0531        | 2.0   | 8666  | 0.0367          | 0.1355 | 0.0619 | 0.1355 | 0.1357    | 17.8214 |
+| 0.0406        | 3.0   | 12999 | 0.0350          | 0.1355 | 0.0619 | 0.1355 | 0.1357    | 17.8213 |
+| 0.0342        | 4.0   | 17332 | 0.0328          | 0.1355 | 0.0618 | 0.1354 | 0.1356    | 17.8198 |
+| 0.0323        | 5.0   | 21665 | 0.0304          | 0.1355 | 0.0618 | 0.1354 | 0.1356    | 17.8198 |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.1

config.json

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+{
+  "_name_or_path": "Malmika/T5-Small-Sinhala-Sumarization",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "classifier_dropout": 0.0,
+  "d_ff": 2048,
+  "d_kv": 64,
+  "d_model": 512,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "relu",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": false,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 6,
+  "num_heads": 8,
+  "num_layers": 6,
+  "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": "4.35.2",
+  "use_cache": true,
+  "vocab_size": 32128
+}

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"
+}

model.safetensors

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runs/Feb11_16-09-21_15a2d3c03059/events.out.tfevents.1707667762.15a2d3c03059.255.0

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special_tokens_map.json

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+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
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+  "pad_token": "[PAD]",
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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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