Update README.md

README.md

@@ -7,20 +7,13 @@ datasets:
 ---
 
 
-# Twitter-roBERTa-base for Sentiment Analysis - UPDATED (2022)
-
-This is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021, and finetuned for sentiment analysis with the TweetEval benchmark. 
-The original Twitter-based RoBERTa model can be found [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m) and the original reference paper is [TweetEval](https://github.com/cardiffnlp/tweeteval). This model is suitable for English. 
-
-- Reference Paper: [TimeLMs paper](https://arxiv.org/abs/2202.03829). 
-- Git Repo: [TimeLMs official repository](https://github.com/cardiffnlp/timelms).
+# Twitter-roBERTa-base for Sentiment Analysis 
 
 <b>Labels</b>: 
 0 -> Negative;
 1 -> Neutral;
 2 -> Positive
 
-This sentiment analysis model has been integrated into [TweetNLP](https://github.com/cardiffnlp/tweetnlp). You can access the demo [here](https://tweetnlp.org).
 
 ## Example Pipeline
 ```python
@@ -31,102 +24,3 @@ sentiment_task("Covid cases are increasing fast!")
 ```
 [{'label': 'Negative', 'score': 0.7236}]
 ```
-
-## Full classification example
-
-```python
-from transformers import AutoModelForSequenceClassification
-from transformers import TFAutoModelForSequenceClassification
-from transformers import AutoTokenizer, AutoConfig
-import numpy as np
-from scipy.special import softmax
-# 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)
-MODEL = f"cardiffnlp/twitter-roberta-base-sentiment-latest"
-tokenizer = AutoTokenizer.from_pretrained(MODEL)
-config = AutoConfig.from_pretrained(MODEL)
-# PT
-model = AutoModelForSequenceClassification.from_pretrained(MODEL)
-#model.save_pretrained(MODEL)
-text = "Covid cases are increasing fast!"
-text = preprocess(text)
-encoded_input = tokenizer(text, return_tensors='pt')
-output = model(**encoded_input)
-scores = output[0][0].detach().numpy()
-scores = softmax(scores)
-# # TF
-# model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
-# model.save_pretrained(MODEL)
-# text = "Covid cases are increasing fast!"
-# encoded_input = tokenizer(text, return_tensors='tf')
-# output = model(encoded_input)
-# scores = output[0][0].numpy()
-# scores = softmax(scores)
-# Print labels and scores
-ranking = np.argsort(scores)
-ranking = ranking[::-1]
-for i in range(scores.shape[0]):
-    l = config.id2label[ranking[i]]
-    s = scores[ranking[i]]
-    print(f"{i+1}) {l} {np.round(float(s), 4)}")
-```
-
-Output: 
-
-```
-1) Negative 0.7236
-2) Neutral 0.2287
-3) Positive 0.0477
-```
-
-
-### References 
-```
-@inproceedings{camacho-collados-etal-2022-tweetnlp,
-    title = "{T}weet{NLP}: Cutting-Edge Natural Language Processing for Social Media",
-    author = "Camacho-collados, Jose  and
-      Rezaee, Kiamehr  and
-      Riahi, Talayeh  and
-      Ushio, Asahi  and
-      Loureiro, Daniel  and
-      Antypas, Dimosthenis  and
-      Boisson, Joanne  and
-      Espinosa Anke, Luis  and
-      Liu, Fangyu  and
-      Mart{\'\i}nez C{\'a}mara, Eugenio" and others,
-    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
-    month = dec,
-    year = "2022",
-    address = "Abu Dhabi, UAE",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2022.emnlp-demos.5",
-    pages = "38--49"
-}
-
-```
-
-```
-@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"
-}
-
-```