End of training

README.md

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+---
+license: apache-2.0
+base_model: t5-small
+tags:
+- generated_from_trainer
+datasets:
+- billsum
+metrics:
+- rouge
+model-index:
+- name: Text_Summarization
+  results:
+  - task:
+      name: Sequence-to-sequence Language Modeling
+      type: text2text-generation
+    dataset:
+      name: billsum
+      type: billsum
+      config: default
+      split: ca_test
+      args: default
+    metrics:
+    - name: Rouge1
+      type: rouge
+      value: 0.1447
+---
+
+<!-- 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. -->
+
+# Text_Summarization
+
+This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.5015
+- Rouge1: 0.1447
+- Rouge2: 0.0522
+- Rougel: 0.1204
+- Rougelsum: 0.1202
+- Gen Len: 19.0
+
+## 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: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 4
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
+| No log        | 1.0   | 62   | 2.7888          | 0.1267 | 0.0351 | 0.1053 | 0.1051    | 19.0    |
+| No log        | 2.0   | 124  | 2.5770          | 0.1336 | 0.0452 | 0.1108 | 0.1107    | 19.0    |
+| No log        | 3.0   | 186  | 2.5178          | 0.1439 | 0.0513 | 0.1188 | 0.1185    | 19.0    |
+| No log        | 4.0   | 248  | 2.5015          | 0.1447 | 0.0522 | 0.1204 | 0.1202    | 19.0    |
+
+
+### Framework versions
+
+- Transformers 4.33.1
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.13.3

config.json

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+{
+  "_name_or_path": "t5-small",
+  "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": "4.33.1",
+  "use_cache": true,
+  "vocab_size": 32128
+}

generation_config.json

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+  "eos_token_id": 1,
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+  "transformers_version": "4.33.1"
+}

pytorch_model.bin

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