Update model card

README.md

@@ -1,3 +1,60 @@
 ---
+language: multilingual
+widget:
+- text: 🤗🤗🤗<mask>
+- text: 🔥The goal of life is <mask> . 🔥
+- text: Il segreto della vita è l’<mask> . ❤️
+- text: Hasta <mask> 👋!
 license: mit
 ---
+
+
+# Twitter-XLM-Roberta-large
+This is a XLM-T large language model specialised on Twitter. 
+The base model was the multilingual XLM-R and the model was then re-trained on tweets from many different languages until December 2022.
+
+To evaluate this and other LMs on Twitter-specific data, please refer to the [XLM-T main repository](https://github.com/cardiffnlp/xlm-t).
+
+Finally, this model is fully compatible with the [TweetNLP library](https://github.com/cardiffnlp/tweetnlp)
+
+
+```
+
+### BibTeX entry and citation info
+
+More information in the reference papers about [multilingual language models on Twitter](https://aclanthology.org/2022.lrec-1.27/) and [time-specific models](https://aclanthology.org/2022.acl-demo.25/).
+Please cite the relevant reference papers if you use this model. 
+
+```bibtex
+@inproceedings{barbieri-etal-2022-xlm,
+    title = "{XLM}-{T}: Multilingual Language Models in {T}witter for Sentiment Analysis and Beyond",
+    author = "Barbieri, Francesco  and
+      Espinosa Anke, Luis  and
+      Camacho-Collados, Jose",
+    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
+    month = jun,
+    year = "2022",
+    address = "Marseille, France",
+    publisher = "European Language Resources Association",
+    url = "https://aclanthology.org/2022.lrec-1.27",
+    pages = "258--266",
+    abstract = "Language models are ubiquitous in current NLP, and their multilingual capacity has recently attracted considerable attention. However, current analyses have almost exclusively focused on (multilingual variants of) standard benchmarks, and have relied on clean pre-training and task-specific corpora as multilingual signals. In this paper, we introduce XLM-T, a model to train and evaluate multilingual language models in Twitter. In this paper we provide: (1) a new strong multilingual baseline consisting of an XLM-R (Conneau et al. 2020) model pre-trained on millions of tweets in over thirty languages, alongside starter code to subsequently fine-tune on a target task; and (2) a set of unified sentiment analysis Twitter datasets in eight different languages and a XLM-T model trained on this dataset.",
+}
+
+@inproceedings{loureiro-etal-2022-timelms,
+    title = "{T}ime{LM}s: Diachronic Language Models from {T}witter",
+    author = "Loureiro, Daniel  and
+      Barbieri, Francesco  and
+      Neves, Leonardo  and
+      Espinosa Anke, Luis  and
+      Camacho-collados, Jose",
+    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
+    month = may,
+    year = "2022",
+    address = "Dublin, Ireland",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.acl-demo.25",
+    doi = "10.18653/v1/2022.acl-demo.25",
+    pages = "251--260",
+    abstract = "Despite its importance, the time variable has been largely neglected in the NLP and language model literature. In this paper, we present TimeLMs, a set of language models specialized on diachronic Twitter data. We show that a continual learning strategy contributes to enhancing Twitter-based language models{'} capacity to deal with future and out-of-distribution tweets, while making them competitive with standardized and more monolithic benchmarks. We also perform a number of qualitative analyses showing how they cope with trends and peaks in activity involving specific named entities or concept drift. TimeLMs is available at github.com/cardiffnlp/timelms.",
+}