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Akhilsplendid
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T5-model

Text2Text Generation
Transformers
PyTorch
t5
Generated from Trainer
text-generation-inference
Inference Endpoints
Model card Files Community
Akhilsplendid commited on
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Training complete!

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Files changed (9) hide show
  1. README.md +71 -0
  2. config.json +62 -0
  3. generation_config.json +7 -0
  4. pytorch_model.bin +3 -0
  5. special_tokens_map.json +107 -0
  6. spiece.model +3 -0
  7. tokenizer.json +0 -0
  8. tokenizer_config.json +112 -0
  9. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: philschmid/flan-t5-base-samsum
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: T5-model
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+ results: []
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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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+ # T5-model
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+
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+ This model is a fine-tuned version of [philschmid/flan-t5-base-samsum](https://huggingface.co/philschmid/flan-t5-base-samsum) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.7013
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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: 1
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+ - eval_batch_size: 1
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+ - seed: 42
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+ - gradient_accumulation_steps: 16
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+ - total_train_batch_size: 16
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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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+ - lr_scheduler_warmup_steps: 500
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+ - num_epochs: 10
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:-----:|:----:|:---------------:|
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+ | 0.9849 | 0.8 | 10 | 0.8062 |
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+ | 0.9748 | 1.61 | 20 | 0.8026 |
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+ | 0.9772 | 2.41 | 30 | 0.7968 |
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+ | 0.979 | 3.22 | 40 | 0.7889 |
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+ | 0.9729 | 4.02 | 50 | 0.7793 |
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+ | 0.9479 | 4.82 | 60 | 0.7687 |
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+ | 0.9111 | 5.63 | 70 | 0.7577 |
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+ | 0.8956 | 6.43 | 80 | 0.7460 |
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+ | 0.8768 | 7.24 | 90 | 0.7338 |
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+ | 0.8566 | 8.04 | 100 | 0.7224 |
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+ | 0.8342 | 8.84 | 110 | 0.7120 |
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+ | 0.8273 | 9.65 | 120 | 0.7013 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.32.0
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+ - Pytorch 2.0.1+cu118
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+ - Datasets 2.14.4
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+ - Tokenizers 0.13.3
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+ {
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+ "_name_or_path": "philschmid/flan-t5-base-samsum",
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+ "architectures": [
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+ "T5ForConditionalGeneration"
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+ ],
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+ "classifier_dropout": 0.0,
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+ "d_ff": 2048,
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+ "d_kv": 64,
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+ "d_model": 768,
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+ "decoder_start_token_id": 0,
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+ "dense_act_fn": "gelu_new",
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+ "dropout_rate": 0.1,
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+ "eos_token_id": 1,
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+ "feed_forward_proj": "gated-gelu",
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+ "initializer_factor": 1.0,
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+ "is_encoder_decoder": true,
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+ "is_gated_act": true,
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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": 12,
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+ "num_heads": 12,
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+ "num_layers": 12,
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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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+ "no_repeat_ngram_size": 3,
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+ "num_beams": 4,
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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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+ "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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+ "tie_word_embeddings": false,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.32.0",
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+ "use_cache": true,
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+ "vocab_size": 32128
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+ }
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