Update README.md

README.md

@@ -4,12 +4,13 @@ datasets:
 language:
 - en
 pipeline_tag: summarization
+license: mit
 ---
 # Model Card for roemmele/ablit-bart-base
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-This model is initialized from facebook/bart-base. It has been fine-tuned on the AbLit dataset, which consists of abridged versions of books aligned with their original versions at the passage level. Given a text, the model generates an abridgement of the text based on what it has observed in AbLit. See the cited paper for more details.
+This model is initialized from facebook/bart-base. It has been fine-tuned on the AbLit dataset, which consists of abridged versions of books aligned with their original versions at the passage level. Given a text, the model generates an abridgement of the text based on what it has observed in AbLit. See the paper cited below for more details.
 
 ## Model Details
 
@@ -20,15 +21,15 @@ This model is initialized from facebook/bart-base. It has been fine-tuned on the
 - **Developed by:** Language Weaver (Melissa Roemmele, Kyle Shaffer, Katrina Olsen, Yiyi Wang, and Steve DeNeefe)
 - **Model type:** Seq2SeqLM
 - **Language(s) (NLP):** English
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** facebook/bart-base
+- **License:** mit
+- **Finetuned from model:** [facebook/bart-base](https://huggingface.co/facebook/bart-base)
 
-### Model Sources [optional]
+### Model Sources
 
 <!-- Provide the basic links for the model. -->
 
 - **Repository:** [github.com/roemmele/AbLit](https://github.com/roemmele/AbLit)
-- **Paper [optional]:** [AbLit: A Resource for Analyzing and Generating Abridged Versions of English Literature](https://arxiv.org/pdf/2302.06579.pdf)
+- **Paper:** [AbLit: A Resource for Analyzing and Generating Abridged Versions of English Literature](https://arxiv.org/pdf/2302.06579.pdf)
 
 ## Uses
 
@@ -81,12 +82,9 @@ data = load_dataset("roemmele/ablit", "chunks-10-sentences")
 
 We used the training script [here](https://github.com/huggingface/transformers/blob/main/examples/pytorch/summarization/run_summarization.py). 
 
-Hyperparameter settings: We specified maximum length of 1024 for both the source (original passage) and target (abridged passage), and truncated all tokens beyond this limit. We evaluated each model on the AbLit development set after each epoch and concluded training when cross-entropy loss stopped decreasing. We used a batch size of 4. For all other hyperparameters we used the default values set by this script. 
-
-
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+We specified maximum length of 1024 for both the source (original passage) and target (abridged passage), and truncated all tokens beyond this limit. We evaluated each model on the AbLit development set after each epoch and concluded training when cross-entropy loss stopped decreasing. We used a batch size of 4. For all other hyperparameters we used the default values set by this script. 
 
 #### Speeds, Sizes, Times
 
@@ -109,7 +107,7 @@ The model obtained a ROUGE-L score of 0.78 on the AbLit test set. See the paper
 
 Our analysis shows that in comparison with human-authored abridgements, the model-generated abridgements tend to preserve more of the original text, suggesting it is challenging to learn what text can be removed while maintaining loyalty to the important parts of the original text.  
 
-## Citation [optional]
+## Citation
 
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
@@ -133,4 +131,4 @@ Melissa Roemmele
 
 ## Model Card Contact
 
-[email protected]
+[email protected]