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reddit_gen_final
sentientconch/reddit_gen_final
reddit_gen_final is a text generation model from sentientconch. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Generation
- Library
- transformers
- License
- mit
- Model type
- gpt2
- Access
- Public
- Created
- May 11, 2023
- Updated
- May 12, 2023
- SHA
- 7cabe6be
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Model card
From the Hugging Face model README
<!-- 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. -->
reddit_gen_final
This model is a fine-tuned version of microsoft/DialoGPT-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.5050
- Rouge: {'rouge1': 0.5318334167452433, 'rouge2': 0.3266503490464716, 'rougeL': 0.4940196552424935, 'rougeLsum': 0.49965823775029017}
- Perplexity: 810.2161
- Bleu: {'bleu': 0.3233116246700081, 'precisions': [0.5456588886510291, 0.3399931653275477, 0.273607307447275, 0.2384403661808989], 'brevity_penalty': 0.9747575251310703, 'length_ratio': 0.975070821529745, 'translation_length': 130796, 'reference_length': 134140}
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: 0.001
- train_batch_size: 1024
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 32768
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1077
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge | Perplexity | Bleu |
|---|---|---|---|---|---|---|
| 3.9872 | 18.02 | 320 | 3.1407 | {'rouge1': 0.4236605668840905, 'rouge2': 0.17676647154634773, 'rougeL': 0.37369585565755137, 'rougeLsum': 0.38069226779493875} | 1007.2803 | {'bleu': 0.1795660368641941, 'precisions': [0.4476203532341719, 0.1996849620846328, 0.12947506323794772, 0.097952801903731], 'brevity_penalty': 0.9786100068791068, 'length_ratio': 0.9788355449530342, 'translation_length': 131301, 'reference_length': 134140} |
| 3.0112 | 37.02 | 640 | 2.6693 | {'rouge1': 0.5006690963461402, 'rouge2': 0.2845737029774397, 'rougeL': 0.4598926127632702, 'rougeLsum': 0.46623659707701914} | 891.6387 | {'bleu': 0.28259351848586683, 'precisions': [0.5153005174673647, 0.2977358252901072, 0.22869830241856198, 0.19400129812455164], 'brevity_penalty': 0.9838352619991267, 'length_ratio': 0.9839645146861488, 'translation_length': 131989, 'reference_length': 134140} |
| 2.5776 | 56.02 | 960 | 2.5050 | {'rouge1': 0.5318334167452433, 'rouge2': 0.3266503490464716, 'rougeL': 0.4940196552424935, 'rougeLsum': 0.49965823775029017} | 810.2161 | {'bleu': 0.3233116246700081, 'precisions': [0.5456588886510291, 0.3399931653275477, 0.273607307447275, 0.2384403661808989], 'brevity_penalty': 0.9747575251310703, 'length_ratio': 0.975070821529745, 'translation_length': 130796, 'reference_length': 134140} |
Framework versions
- Transformers 4.28.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
- Task
- Text Generation
- Library
- transformers
- Type
- gpt2
- License
- mit
- Created
- May 11, 2023
- Updated
- May 12, 2023