Update to 1M parameter model
Browse files- README.md +68 -0
- all_results.json +15 -0
- config.json +4 -11
- eval_results.json +10 -0
- optimizer.pt +0 -3
- pytorch_model.bin +2 -2
- rng_state.pth +0 -3
- scheduler.pt +0 -3
- special_tokens_map.json +21 -3
- tokenizer.json +1 -1
- tokenizer_config.json +2 -2
- train_results.json +8 -0
- trainer_state.json +110 -101
- training_args.bin +2 -2
README.md
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---
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tags:
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- generated_from_trainer
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datasets:
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- wikitext
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metrics:
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- accuracy
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model-index:
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- name: output_tiny
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results:
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- task:
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name: Causal Language Modeling
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type: text-generation
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dataset:
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name: wikitext wikitext-103-v1
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type: wikitext
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args: wikitext-103-v1
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.2132901596611274
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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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# output_tiny
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This model is a fine-tuned version of [gpt2_tiny_random](https://huggingface.co/gpt2_tiny_random) on the wikitext wikitext-103-v1 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 5.3359
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- Accuracy: 0.2133
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0001
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- train_batch_size: 32
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- eval_batch_size: 64
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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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- training_steps: 50000
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### Training results
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### Framework versions
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- Transformers 4.25.1
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- Pytorch 1.13.1
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- Datasets 2.8.0
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- Tokenizers 0.13.2
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all_results.json
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{
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"epoch": 14.01,
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"eval_accuracy": 0.2132901596611274,
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"eval_loss": 5.335912704467773,
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"eval_runtime": 3.848,
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"eval_samples": 240,
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"eval_samples_per_second": 62.37,
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"eval_steps_per_second": 1.039,
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"perplexity": 207.6621965544685,
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"train_loss": 5.854185776367188,
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"train_runtime": 10672.3435,
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"train_samples": 114149,
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"train_samples_per_second": 149.92,
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"train_steps_per_second": 4.685
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}
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config.json
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{
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"_name_or_path": "
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"activation_function": "gelu_new",
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"architectures": [
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"GPT2LMHeadModel"
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"model_type": "gpt2",
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"
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"n_embd": 2,
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"n_head": 2,
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"n_inner":
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"n_layer":
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"n_positions": 1024,
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"reorder_and_upcast_attn": false,
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"resid_pdrop": 0.1,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"task_specific_params": {
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"text-generation": {
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"do_sample": true,
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"max_length": 50
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}
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},
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"torch_dtype": "float32",
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"transformers_version": "4.25.1",
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"use_cache": true,
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{
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"_name_or_path": "gpt2_tiny_random",
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"activation_function": "gelu_new",
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"architectures": [
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"GPT2LMHeadModel"
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"model_type": "gpt2",
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"n_embd": 32,
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"n_head": 2,
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"n_inner": 64,
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"n_layer": 4,
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"n_positions": 1024,
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"reorder_and_upcast_attn": false,
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"resid_pdrop": 0.1,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"torch_dtype": "float32",
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"transformers_version": "4.25.1",
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"use_cache": true,
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eval_results.json
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{
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"epoch": 14.01,
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"eval_accuracy": 0.2132901596611274,
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"eval_loss": 5.335912704467773,
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"eval_runtime": 3.848,
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"eval_samples": 240,
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"eval_samples_per_second": 62.37,
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"eval_steps_per_second": 1.039,
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"perplexity": 207.6621965544685
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}
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optimizer.pt
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pytorch_model.bin
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rng_state.pth
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scheduler.pt
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special_tokens_map.json
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tokenizer.json
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tokenizer_config.json
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train_results.json
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