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README.md

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----
-language: english
-widget:
-- text: "Covid cases are increasing fast!"
----
-
-
-# Twitter-roBERTa-base for Sentiment Analysis - UPDATED (2021)
-
-This is a roBERTa-base model trained on ~124M tweets from January 2018 to December 2021 (see [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m)), and finetuned for sentiment analysis with the TweetEval benchmark. 
-The original roBERTa-base 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).
-
-<b>Labels</b>: 
-0 -> Negative;
-1 -> Neutral;
-2 -> Positive
-
-## Example Pipeline
-```python
-from transformers import pipeline
-sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path)
-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
-```