added dec gpt2

README.md

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+---
+language: en
+tags:
+- exbert
+
+---
+
+
+# OLM GPT-2 December 2022
+
+This is a more up-to-date version of the [original GPT-2](https://huggingface.co/gpt2).
+In addition to being more up-to-date, it also tends to perform better than the original GPT2 on standard benchmarks.
+It was trained on a cleaned December 2022 snapshot of Common Crawl and Wikipedia.
+
+This model was created as part of the OLM project, which has the goal of continuously training and releasing models that are up-to-date and comparable in standard language model performance to their static counterparts.
+This is important because we want our models to know about events like COVID or 
+a presidential election right after they happen.
+
+## Intended uses
+
+You can use the raw model for text generation or fine-tune it to a downstream task.
+
+## How to use
+
+You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
+set a seed for reproducibility:
+
+```python
+>>> from transformers import pipeline, set_seed
+>>> # It is important to include the bad_words_ids=[[0,2]] if you want this model to stay on topic.
+>>> # Otherwise, the model may generate start and end tokens followed by text that is not relevant to
+>>> # the previous text.
+>>> generator = pipeline('text-generation', model='olm/olm-gpt2-dec-2022', bad_words_ids=[[0,2]])
+>>> set_seed(42)
+>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)
+[{'generated_text': "Hello, I'm a language model, but you want to know if I have a language in that language. Is this possible? Please explain"},
+ {'generated_text': "Hello, I'm a language model, and here's some useful news for you all: The C++ API is becoming more and more popular for"},
+ {'generated_text': "Hello, I'm a language model, I'm not trying to learn or understand a new tool, my job is to be as happy as"},
+ {'generated_text': "Hello, I'm a language model, a language analyst, and a language system designer. I'm just a curious guy.\n"},
+ {'generated_text': "Hello, I'm a language model, I'm not doing anything that needs to be done for the current time (or previous)."}]
+```
+
+Here is how to use this model to get the features of a given text in PyTorch:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained('olm/olm-gpt2-dec-2022')
+model = AutoModelForCausalLM.from_pretrained('olm/olm-gpt2-dec-2022')
+text = "Replace me by any text you'd like."
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+```
+
+## Dataset
+
+The model and tokenizer were trained with this [December 2022 cleaned Common Crawl dataset](https://huggingface.co/datasets/olm/olm-CC-MAIN-2022-49-sampling-ratio-olm-0.15114822547) plus this [December 2022 cleaned Wikipedia dataset](https://huggingface.co/datasets/olm/olm-wikipedia-20221220).\
+The tokenized version of these concatenated datasets is [here](https://huggingface.co/datasets/olm/olm-december-2022-tokenized-1024).\
+The datasets were created with this [repo](https://github.com/huggingface/olm-datasets).
+
+## Training
+
+The model was trained according to the OLM GPT2 instructions at this [repo](https://github.com/huggingface/olm-training).
+
+## Evaluation results
+
+The model achieves the following results without any fine-tuning (zero-shot):
+
+| Task        | Metric     | Original GPT2       | OLM GPT2 Dec 2022 (Ours) | Significance of Difference (two-tailed p-value) |
+|:------------|:-----------|--------------------:|-------------------------:|----------------------------------:|
+|rte          |acc         |0.5307               |0.5199                    |0.7184                             |
+|piqa         |acc/acc_norm|0.6289/0.6251        |**0.6692**/**0.6665**     |**0.0004**/**0.0003**              |
+|copa         |acc         |0.6400               |0.6800                    |0.4070                             |
+|record       |f1/em       |**0.7094**/**0.7026**|0.6884/0.6818             |**0.0000**/**0.0000**              |
+|boolq        |acc         |0.4872               |**0.6021**                |**0.0000**                         |
+|cb           |acc/f1      |0.4107/0.2619        |0.3393/0.1840             |0.2816/NA                          |
+|hellaswag    |acc/acc_norm|0.2892/0.3114        |**0.3079**/**0.3482**     |**0.0000**/**0.0000**              |
+|mrpc         |acc/f1      |0.5662/0.6911        |**0.6814**/**0.8099**     |**0.0000**/**0.0000**              |
+|multirc      |acc         |0.0189               |0.0220                    |0.4755                             |
+|lambada      |ppl/acc     |40.0554/0.3256       |**28.3359**/**0.3699**    |**0.0000**/**0.0000**              |
+|wsc          |acc         |0.4327               |0.3654                    |0.1680                             |
+|wic          |acc         |0.4922               |0.5000                    |0.6924                             |
+|mnli         |acc         |0.3372               |**0.3501**                |**0.0071**                         |
+|qnli         |acc         |0.5017               |0.4946                    |0.2913                             |
+|cola         |mcc         |0.0126               |0.0000                    |0.6880                             |
+|triviaqa     |acc         |0.0151               |**0.0181**                |**0.0088**                         |
+|winogrande   |acc         |0.5162               |0.5051                    |0.4314                             |
+|webqs        |acc         |0.0030               |**0.0079**                |**0.0000**                         |
+|arc_easy     |acc/acc_norm|0.4381/0.3948        |**0.4693**/**0.4230**     |**0.0022**/**0.0049**              |
+|arc_challenge|acc/acc_norm|0.1903/0.2270        |0.2090/0.2398             |0.1017/0.2957                      |
+
+To get these results, we used commit `f079e322b857714fcef1ada9e78ddc606fe51e84` of the Eleuther AI evaluation harness [here](https://github.com/EleutherAI/lm-evaluation-harness),
+which can produce results different than those reported in the GPT2 paper.
+We added a change [here](https://github.com/EleutherAI/lm-evaluation-harness/compare/master...mathemakitten:lm-evaluation-harness:master) to enable evaluation of the OLM GPT2, which has a very slightly different vocab size.
+The p-values come from the stderr from the evaluation harness, plus a normal distribution assumption.

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,
+  "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,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "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
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.24.0",
+  "use_cache": true,
+  "vocab_size": 50265
+}

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special_tokens_map.json

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