Update README.md

README.md

@@ -1,43 +1,53 @@
----
-language: en
-license: mit
-pipeline_tag: text-generation
----
-# GPT-Neo 1.3B - Adventure
-## Model Description
-GPT-Neo 1.3B-Adventure is a finetune created using EleutherAI's GPT-Neo 1.3B model.
-## Training data
-The training data is a direct copy of the "cys" dataset by VE, a CYOA-based dataset.
-### 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-Neo-1.3B-Adventure')
->>> generator("> You wake up.", do_sample=True, min_length=50)
-[{'generated_text': '> You wake up"\nYou get out of bed, don your armor and get out of the door in search for new adventures.'}]
-```
-### Limitations and Biases
-GPT-Neo was trained as an autoregressive language model. This means that its core functionality 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.
-GPT-Neo was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending on your usecase GPT-Neo may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper 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-Neo 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 is made using the following software:
-```bibtex
-@software{gpt-neo,
-  author       = {Black, Sid and
-                  Leo, Gao and
-                  Wang, Phil and
-                  Leahy, Connor and
-                  Biderman, Stella},
-  title        = {{GPT-Neo: Large Scale Autoregressive Language 
-                   Modeling with Mesh-Tensorflow}},
-  month        = mar,
-  year         = 2021,
-  note         = {{If you use this software, please cite it using 
-                   these metadata.}},
-  publisher    = {Zenodo},
-  version      = {1.0},
-  doi          = {10.5281/zenodo.5297715},
-  url          = {https://doi.org/10.5281/zenodo.5297715}
-}
+---
+language: en
+license: mit
+pipeline_tag: text-generation
+datasets:
+- fka/awesome-chatgpt-prompts
+metrics:
+- accuracy
+library_name: adapter-transformers
+tags:
+- art
+- legal
+- code
+- text-generation-inference
+---
+# GPT-Neo 1.3B - Adventure
+## Model Description
+GPT-Neo 1.3B-Adventure is a finetune created using EleutherAI's GPT-Neo 1.3B model.
+## Training data
+The training data is a direct copy of the "cys" dataset by VE, a CYOA-based dataset.
+### 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-Neo-1.3B-Adventure')
+>>> generator("> You wake up.", do_sample=True, min_length=50)
+[{'generated_text': '> You wake up"\nYou get out of bed, don your armor and get out of the door in search for new adventures.'}]
+```
+### Limitations and Biases
+GPT-Neo was trained as an autoregressive language model. This means that its core functionality 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.
+GPT-Neo was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending on your usecase GPT-Neo may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper 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-Neo 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 is made using the following software:
+```bibtex
+@software{gpt-neo,
+  author       = {Black, Sid and
+                  Leo, Gao and
+                  Wang, Phil and
+                  Leahy, Connor and
+                  Biderman, Stella},
+  title        = {{GPT-Neo: Large Scale Autoregressive Language 
+                   Modeling with Mesh-Tensorflow}},
+  month        = mar,
+  year         = 2021,
+  note         = {{If you use this software, please cite it using 
+                   these metadata.}},
+  publisher    = {Zenodo},
+  version      = {1.0},
+  doi          = {10.5281/zenodo.5297715},
+  url          = {https://doi.org/10.5281/zenodo.5297715}
+}
 ```