Update README.md

README.md

@@ -9,4 +9,48 @@ tags:
 - token classification
 - named entity recognition
 pipeline_tag: token-classification
----
+widget:
+- text: "The share note file feature is completely useless."
+  example_title: "Example 1"
+- text: "Great app I've tested a lot of free habit tracking apps and this is by far my favorite."
+  example_title: "Example 2"
+- text: "The only negative feedback I can give about this app is the difficulty level to set a sleep timer on it."
+  example_title: "Example 3"
+- text: "Does what you want with a small pocket size checklist reminder app"
+  example_title: "Example 4"
+- text: "Very bad because call recording notification send other person"
+  example_title: "Example 5"
+- text: "I originally downloaded the app for pomodoro timing, but I stayed for the project management features, with syncing."
+  example_title: "Example 6"
+- text: "It works accurate and I bought a portable one lap gps tracker it have a great battery Life"
+  example_title: "Example 7"
+- text: "I'm my phone the notifications of group message are not at a time please check what was the reason behind it because due to this default I loose some opportunity"
+  example_title: "Example 8"
+- text: "There is no setting for recurring alarms"
+  example_title: "Example 9"
+---
+
+# T-FREX RoBERTa base model
+
+T-FREX is a transformer-based feature extraction method for mobile app reviews based on fine-tuning Large Language Models (LLMs) for a named entity recognition task. We collect a dataset of ground truth features from users in a real crowdsourced software recommendation platform, and we use this dataset to fine-tune multiple LLMs under different data configurations. We assess the performance of T-FREX with respect to this ground truth, and we complement our analysis by comparing T-FREX with a baseline method from the field. Finally, we assess the quality of new features predicted by T-FREX through an external human evaluation. Results show that T-FREX outperforms on average the traditional syntactic-based method, especially when discovering new features from a domain for which the model has been fine-tuned.
+
+Source code for data generation, fine-tuning and model inference are available in the original [GitHub repository](https://github.com/gessi-chatbots/t-frex/).
+
+## Model description
+
+This version of T-FREX has been fine-tuned for [token classification](https://huggingface.co/docs/transformers/tasks/token_classification#train) from [BERT large model (uncased)](https://huggingface.co/bert-base-uncased).
+
+## Model variations
+
+T-FREX includes a set of released, fine-tuned models which are compared in the original study (to be published).
+
+- [**t-frex-bert-base-uncased**](https://huggingface.co/quim-motger/t-frex-bert-base-uncased)
+- [**t-frex-bert-large-uncased**](https://huggingface.co/quim-motger/t-frex-bert-large-uncased)
+- [**t-frex-roberta-base**](https://huggingface.co/quim-motger/t-frex-roberta-base)
+- [**t-frex-roberta-large**](https://huggingface.co/quim-motger/t-frex-roberta-large)
+- [**t-frex-xlnet-base-cased**](https://huggingface.co/quim-motger/t-frex-xlnet-base-cased)
+- [**t-frex-xlnet-large-cased**](https://huggingface.co/quim-motger/t-frex-xlnet-large-cased)
+
+## How to use
+
+You can use this model following the instructions for [model inference for token classification](https://huggingface.co/docs/transformers/tasks/token_classification#inference).