Update README.md

README.md

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   example_title: "Example 8"
 - text: "There is no setting for recurring alarms"
   example_title: "Example 9"
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+---
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+# T-FREX BERT base model (uncased)
+
+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 base 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).