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Model: cambridgeltl/simctg_rocstories
Source: Original Platform

.gitattributes

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README.md

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+This model provides a GPT-2 language model trained with SimCTG on the ROCStories benchmark [(Mostafazadeh et al., 2016)](https://aclanthology.org/N16-1098.pdf) based on our paper [_A Contrastive Framework for Neural Text Generation_](https://arxiv.org/abs/2202.06417).
+
+We provide a detailed tutorial on how to apply SimCTG and Contrastive Search in our [project repo](https://github.com/yxuansu/SimCTG#4-huggingface-style-tutorials-back-to-top). In the following, we illustrate a brief tutorial on how to use our approach to perform text generation.
+
+## 1. Installation of SimCTG:
+```yaml
+pip install simctg --upgrade
+```
+
+## 2. Initialize SimCTG Model:
+```python
+import torch
+# load SimCTG language model
+from simctg.simctggpt import SimCTGGPT
+model_name = r'cambridgeltl/simctg_rocstories'
+model = SimCTGGPT(model_name)
+model.eval()
+tokenizer = model.tokenizer
+```
+
+## 3. Prepare the Text Prefix:
+```python
+prompt = r"Accident in the Lab <|endoftext|>"
+print ('Prefix is: {}'.format(prompt))
+tokens = model.tokenizer.tokenize(prompt)
+input_ids = model.tokenizer.convert_tokens_to_ids(tokens)
+input_ids = torch.LongTensor(input_ids).view(1,-1)
+```
+
+## 4. Generate Text with Contrastive Search:
+```python
+beam_width, alpha, decoding_len = 5, 0.65, 45
+output = model.fast_contrastive_search(input_ids=input_ids, beam_width=beam_width, 
+                                       alpha=alpha, decoding_len=decoding_len) 
+print("Output:\n" + 100 * '-')
+print(tokenizer.decode(output).split(model.tokenizer.eos_token)[1].strip())
+'''
+  Prefix is: Accident in the Lab <|endoftext|>
+  Output:
+  ----------------------------------------------------------------------------------------------------
+  Tom went to work one day. He noticed a lab accident in the lab. Tom was worried about his safety at work. 
+  Unfortunately the accident didn't go well. Tom wound up leaving early to get back on the job.
+'''
+```
+
+For more details of our work, please refer to our main [project repo](https://github.com/yxuansu/SimCTG).
+
+## 5. Citation:
+If you find our paper and resources useful, please kindly leave a star and cite our paper. Thanks!
+
+```bibtex
+@article{su2022contrastive,
+  title={A Contrastive Framework for Neural Text Generation},
+  author={Su, Yixuan and Lan, Tian and Wang, Yan and Yogatama, Dani and Kong, Lingpeng and Collier, Nigel},
+  journal={arXiv preprint arXiv:2202.06417},
+  year={2022}
+}
+```
+
+

added_tokens.json

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+{"<_PAD_>": 50257}

config.json

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+{
+  "_name_or_path": "gpt2",
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 50256,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 50256,
+  "gradient_checkpointing": false,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 12,
+  "n_positions": 1024,
+  "resid_pdrop": 0.1,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50
+    }
+  },
+  "transformers_version": "4.7.0",
+  "use_cache": true,
+  "vocab_size": 50258
+}

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special_tokens_map.json

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+{"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}

tokenizer_config.json

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+{"unk_token": "<|endoftext|>", "bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "add_prefix_space": false, "model_max_length": 1024, "special_tokens_map_file": null, "name_or_path": "gpt2"}