update model card README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+metrics:
+- rouge
+- bleu
+model-index:
+- name: reddit_gen_final
+  results: []
+---
+
+<!-- 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](https://huggingface.co/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