Adding Evaluation Results

This is an automated PR created with https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr

The purpose of this PR is to add evaluation results from the Open LLM Leaderboard to your model card.

If you encounter any issues, please report them to https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr/discussions

README.md

@@ -1,42 +1,56 @@
----
-language: en
-license: mit
----
-# GPT-J 6B - Janeway
-## Model Description
-GPT-J 6B-Janeway is a finetune created using EleutherAI's GPT-J 6B model.
-## Training data
-The training data contains around 2210 ebooks, mostly in the sci-fi and fantasy genres. The dataset is based on the same dataset used by GPT-Neo-2.7B-Picard, with 20% more data in various genres.
-Some parts of the dataset have been prepended using the following text: `[Genre: <genre1>,<genre2>]`
-### How to use
-You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
-```py
->>> from transformers import pipeline
->>> generator = pipeline('text-generation', model='KoboldAI/GPT-J-6B-Janeway')
->>> generator("Welcome Captain Janeway, I apologize for the delay.", do_sample=True, min_length=50)
-[{'generated_text': 'Welcome Captain Janeway, I apologize for the delay."\nIt's all right," Janeway said. "I'm certain that you're doing your best to keep me informed of what\'s going on."'}]
-```
-### Limitations and Biases
-
-The core functionality of GPT-J is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. When prompting GPT-J it is important to remember that the statistically most likely next token is often not the token that produces the most "accurate" text. Never depend upon GPT-J to produce factually accurate output.
-
-GPT-J was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending upon use case GPT-J may produce socially unacceptable text. See [Sections 5 and 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a more detailed analysis of the biases in the Pile.
-
-As with all language models, it is hard to predict in advance how GPT-J will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.
-
-### BibTeX entry and citation info
-The model uses the following model as base:
-```bibtex
-@misc{gpt-j,
-  author = {Wang, Ben and Komatsuzaki, Aran},
-  title = {{GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model}},
-  howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}},
-  year = 2021,
-  month = May
-}
-```
-
-## Acknowledgements
-
-This project would not have been possible without compute generously provided by Google through the
-[TPU Research Cloud](https://sites.research.google/trc/), as well as the Cloud TPU team for providing early access to the [Cloud TPU VM](https://cloud.google.com/blog/products/compute/introducing-cloud-tpu-vms) Alpha.
+---
+language: en
+license: mit
+---
+# GPT-J 6B - Janeway
+## Model Description
+GPT-J 6B-Janeway is a finetune created using EleutherAI's GPT-J 6B model.
+## Training data
+The training data contains around 2210 ebooks, mostly in the sci-fi and fantasy genres. The dataset is based on the same dataset used by GPT-Neo-2.7B-Picard, with 20% more data in various genres.
+Some parts of the dataset have been prepended using the following text: `[Genre: <genre1>,<genre2>]`
+### How to use
+You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
+```py
+>>> from transformers import pipeline
+>>> generator = pipeline('text-generation', model='KoboldAI/GPT-J-6B-Janeway')
+>>> generator("Welcome Captain Janeway, I apologize for the delay.", do_sample=True, min_length=50)
+[{'generated_text': 'Welcome Captain Janeway, I apologize for the delay."\nIt's all right," Janeway said. "I'm certain that you're doing your best to keep me informed of what\'s going on."'}]
+```
+### Limitations and Biases
+
+The core functionality of GPT-J is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. When prompting GPT-J it is important to remember that the statistically most likely next token is often not the token that produces the most "accurate" text. Never depend upon GPT-J to produce factually accurate output.
+
+GPT-J was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending upon use case GPT-J may produce socially unacceptable text. See [Sections 5 and 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a more detailed analysis of the biases in the Pile.
+
+As with all language models, it is hard to predict in advance how GPT-J will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.
+
+### BibTeX entry and citation info
+The model uses the following model as base:
+```bibtex
+@misc{gpt-j,
+  author = {Wang, Ben and Komatsuzaki, Aran},
+  title = {{GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model}},
+  howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}},
+  year = 2021,
+  month = May
+}
+```
+
+## Acknowledgements
+
+This project would not have been possible without compute generously provided by Google through the
+[TPU Research Cloud](https://sites.research.google/trc/), as well as the Cloud TPU team for providing early access to the [Cloud TPU VM](https://cloud.google.com/blog/products/compute/introducing-cloud-tpu-vms) Alpha.
+
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_KoboldAI__GPT-J-6B-Janeway)
+
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 34.57   |
+| ARC (25-shot)         | 40.87          |
+| HellaSwag (10-shot)   | 67.11    |
+| MMLU (5-shot)         | 27.45         |
+| TruthfulQA (0-shot)   | 35.74   |
+| Winogrande (5-shot)   | 64.72   |
+| GSM8K (5-shot)        | 1.36        |
+| DROP (3-shot)         | 4.76         |