add model

.gitignore

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+checkpoint-*/

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- wsj_markets
+metrics:
+- rouge
+model_index:
+- name: t5-small-finetuned-xsum
+  results:
+  - task:
+      name: Sequence-to-sequence Language Modeling
+      type: text2text-generation
+    dataset:
+      name: wsj_markets
+      type: wsj_markets
+      args: default
+    metric:
+      name: Rouge1
+      type: rouge
+      value: 10.4492
+---
+
+<!-- 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-finetuned-xsum
+
+This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wsj_markets dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.1447
+- Rouge1: 10.4492
+- Rouge2: 3.9563
+- Rougel: 9.3368
+- Rougelsum: 9.9828
+- 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: 4
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel | Rougelsum | Gen Len |
+|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:|
+| 2.2742        | 1.0   | 868  | 1.3135          | 9.4644  | 2.618  | 8.4048 | 8.9764    | 19.0    |
+| 1.4607        | 2.0   | 1736 | 1.2134          | 9.6327  | 3.8535 | 9.0703 | 9.2466    | 19.0    |
+| 1.3579        | 3.0   | 2604 | 1.1684          | 10.1616 | 3.5498 | 9.2294 | 9.4507    | 19.0    |
+| 1.3314        | 4.0   | 3472 | 1.1514          | 10.0621 | 3.6907 | 9.1635 | 9.4955    | 19.0    |
+| 1.3084        | 5.0   | 4340 | 1.1447          | 10.4492 | 3.9563 | 9.3368 | 9.9828    | 19.0    |
+
+
+### Framework versions
+
+- Transformers 4.8.2
+- Pytorch 1.9.0+cu102
+- Datasets 1.10.0
+- Tokenizers 0.10.3

config.json

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+{
+  "_name_or_path": "t5-small",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "d_ff": 2048,
+  "d_kv": 64,
+  "d_model": 512,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "gradient_checkpointing": false,
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "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_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: "
+    }
+  },
+  "transformers_version": "4.8.2",
+  "use_cache": true,
+  "vocab_size": 32128
+}

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