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Add meta files

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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - searde/dataset-financial-documents-3
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+ metrics:
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+ - rouge
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+ model-index:
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+ - name: tst-summarization
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+ results:
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+ - task:
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+ name: Summarization
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+ type: summarization
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+ dataset:
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+ name: searde/dataset-financial-documents-3 3.0.0
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+ type: searde/dataset-financial-documents-3
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+ config: 3.0.0
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+ split: validation
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+ args: 3.0.0
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+ metrics:
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+ - name: Rouge1
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+ type: rouge
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+ value: 89.9206
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # tst-summarization
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+
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+ This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the searde/dataset-financial-documents-3 3.0.0 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0967
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+ - Rouge1: 89.9206
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+ - Rouge2: 68.4513
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+ - Rougel: 89.6416
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+ - Rougelsum: 89.7796
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+ - Gen Len: 39.0804
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 4
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 3.0
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+
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+ ### Training results
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+
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.31.0.dev0
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+ - Pytorch 2.0.1
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+ - Datasets 2.13.1
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+ - Tokenizers 0.13.3
all_results.json ADDED
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+ {
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+ "epoch": 3.0,
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+ "eval_gen_len": 39.08040201005025,
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+ "eval_loss": 0.09671811014413834,
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+ "eval_rouge1": 89.9206,
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+ "eval_rouge2": 68.4513,
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+ "eval_rougeL": 89.6416,
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+ "eval_rougeLsum": 89.7796,
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+ "eval_runtime": 145.9897,
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+ "eval_samples": 199,
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+ "eval_samples_per_second": 1.363,
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+ "eval_steps_per_second": 0.342,
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+ "train_loss": 0.19635908762613932,
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+ "train_runtime": 197.2934,
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+ "train_samples": 199,
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+ "train_samples_per_second": 3.026,
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+ "train_steps_per_second": 0.76
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+ }
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+ {
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+ "_name_or_path": "t5-small",
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+ "architectures": [
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+ "T5ForConditionalGeneration"
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+ ],
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+ "d_ff": 2048,
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+ "d_kv": 64,
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+ "d_model": 512,
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+ "decoder_start_token_id": 0,
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+ "dense_act_fn": "relu",
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+ "dropout_rate": 0.1,
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+ "layer_norm_epsilon": 1e-06,
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+ "model_type": "t5",
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+ "n_positions": 512,
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+ "num_decoder_layers": 6,
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+ "num_heads": 8,
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+ "num_layers": 6,
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+ "output_past": true,
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+ "pad_token_id": 0,
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+ "relative_attention_max_distance": 128,
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+ "relative_attention_num_buckets": 32,
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+ "task_specific_params": {
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+ "summarization": {
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+ "early_stopping": true,
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+ "length_penalty": 2.0,
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+ "max_length": 200,
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+ "min_length": 30,
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+ "no_repeat_ngram_size": 3,
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+ "num_beams": 4,
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+ "prefix": "summarize: "
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+ },
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+ "translation_en_to_de": {
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+ "early_stopping": true,
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+ "max_length": 300,
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+ "num_beams": 4,
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+ "prefix": "translate English to German: "
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+ },
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+ "translation_en_to_fr": {
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+ "early_stopping": true,
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+ "max_length": 300,
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+ "num_beams": 4,
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+ "prefix": "translate English to French: "
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+ },
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+ "translation_en_to_ro": {
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+ "early_stopping": true,
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+ "max_length": 300,
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+ "num_beams": 4,
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+ "prefix": "translate English to Romanian: "
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+ }
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+ },
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.31.0.dev0",
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+ "use_cache": true,
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+ "vocab_size": 32128
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+ }
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+ {
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+ "eval_gen_len": 39.08040201005025,
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+ "eval_loss": 0.09671811014413834,
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+ "eval_rouge1": 89.9206,
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+ "eval_rouge2": 68.4513,
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+ "eval_rougeL": 89.6416,
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+ "eval_rougeLsum": 89.7796,
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+ "eval_runtime": 145.9897,
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+ "eval_samples": 199,
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+ "eval_samples_per_second": 1.363,
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+ "eval_steps_per_second": 0.342
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+ }
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