.gitattributes

@@ -6,4 +6,3 @@
 *.tar.gz filter=lfs diff=lfs merge=lfs -text
 *.ot filter=lfs diff=lfs merge=lfs -text
 *.onnx filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,23 +1,8 @@
----
+# Twitter-roBERTa-base
-datasets:
-- tweet_eval
-language:
-- en
----
-# Twitter-roBERTa-base for Sentiment Analysis
 
-This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis with the TweetEval benchmark. This model is suitable for English (for a similar multilingual model, see [XLM-T](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment)).
+This is a roBERTa-base model trained on ~58M tweets and finetuned for the Sentiment Analysis task at Semeval 2018. 
-
+For full description: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf). 
-- Reference Paper: [_TweetEval_ (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf). 
+To evaluate this and other models on Twitter-specific data, please refer to the [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).
-- Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).
-
-<b>Labels</b>: 
-0 -> Negative;
-1 -> Neutral;
-2 -> Positive
-
-<b>New!</b> We just released a new sentiment analysis model trained on more recent and a larger quantity of tweets. 
-See [twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) and [TweetNLP](https://tweetnlp.org) for more details.
 
 ## Example of classification
 
@@ -30,17 +15,6 @@ from scipy.special import softmax
 import csv
 import urllib.request
 
-# Preprocess text (username and link placeholders)
-def preprocess(text):
-    new_text = []
- 
- 
-    for t in text.split(" "):
-        t = '@user' if t.startswith('@') and len(t) > 1 else t
-        t = 'http' if t.startswith('http') else t
-        new_text.append(t)
-    return " ".join(new_text)
-
 # Tasks:
 # emoji, emotion, hate, irony, offensive, sentiment
 # stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary
@@ -63,7 +37,6 @@ model = AutoModelForSequenceClassification.from_pretrained(MODEL)
 model.save_pretrained(MODEL)
 
 text = "Good night 😊"
-text = preprocess(text)
 encoded_input = tokenizer(text, return_tensors='pt')
 output = model(**encoded_input)
 scores = output[0][0].detach().numpy()
@@ -95,25 +68,3 @@ Output:
 2) neutral 0.1458
 3) negative 0.0076
 ```
-
-### BibTeX entry and citation info
-
-Please cite the [reference paper](https://aclanthology.org/2020.findings-emnlp.148/) if you use this model.
-
-```bibtex
-@inproceedings{barbieri-etal-2020-tweeteval,
-    title = "{T}weet{E}val: Unified Benchmark and Comparative Evaluation for Tweet Classification",
-    author = "Barbieri, Francesco  and
-      Camacho-Collados, Jose  and
-      Espinosa Anke, Luis  and
-      Neves, Leonardo",
-    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
-    month = nov,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2020.findings-emnlp.148",
-    doi = "10.18653/v1/2020.findings-emnlp.148",
-    pages = "1644--1650"
-}
-```