Update README.md

README.md

@@ -15,7 +15,8 @@ pipeline_tag: text-classification
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-This model is intended to predict emotions (valence, arousal) in written stories. For all details see [the paper (TODO)](#) and [the accompanying github repo](https://github.com/lc0197/emotional_trajectories_stories).
+This model is intended to predict emotions (valence, arousal) in written stories. For all details see [the paper](http://arxiv.org/abs/2406.02251) and 
+[the accompanying github repo](https://github.com/lc0197/emotional_trajectories_stories).
 
 
 
@@ -23,7 +24,7 @@ This model is intended to predict emotions (valence, arousal) in written stories
 
 <!-- Provide a longer summary of what this model is. -->
 
-As described in [the paper (TODO)](#), this model is finetuned from [DeBERTaV3-large](https://huggingface.co/microsoft/deberta-v3-large) and predicts sentence-wise valence/arousal values between 0 and 1. 
+As described in [the paper](http://arxiv.org/abs/2406.02251), this model is finetuned from [DeBERTaV3-large](https://huggingface.co/microsoft/deberta-v3-large) and predicts sentence-wise valence/arousal values between 0 and 1. 
 
 This particular checkpoint was trained with a window size of 4. 
 
@@ -51,14 +52,14 @@ The [accompanying repo](https://github.com/lc0197/emotional_trajectories_stories
 <!-- Provide the basic links for the model. -->
 
 - **Repository:** [Github](https://github.com/lc0197/emotional_trajectories_stories)
-- **Paper:** [ArXiv](#)
+- **Paper:** [ArXiv](http://arxiv.org/abs/2406.02251)
 
 ## Uses
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 This model is intended to predict emotions (valence, arousal) in written stories. It was mainly trained on stories for children. 
 Please note that the model is not production-ready and provided here for demonstration purposes only. 
-For details on the datasets used, please refer to the [paper (TODO)](#).
+For details on the datasets used, please refer to the [paper](http://arxiv.org/abs/2406.02251).
 
 In the [github repository](https://github.com/lc0197/emotional_trajectories_stories), a convenient script to predict V/A in existing texts is provided. Example call: 
 
@@ -69,7 +70,7 @@ python3 predict.py --input_csv input_file.csv --output_csv output_file.csv --che
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
-Please see the *Limitations* section in [the paper](#). Please note that the model is not production-ready and provided here for demonstration purposes only. 
+Please see the *Limitations* section in [the paper](http://arxiv.org/abs/2406.02251). Please note that the model is not production-ready and provided here for demonstration purposes only. 
 
 
 ## Citation [optional]