README.md

---
language: en
datasets:
- tweet_eval
widget:
- text: Covid cases are increasing fast!
model-index:
- name: cardiffnlp/twitter-roberta-base-sentiment-latest
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: tweet_eval
      type: tweet_eval
      config: sentiment
      split: validation
    metrics:
    - type: accuracy
      value: 0.7715
      name: Accuracy
      verified: true
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    - type: f1
      value: 0.7606415252231301
      name: F1 Macro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWIxYzJhNjgxNTM5ZTRjNDJmNTU2NGFlOWE2ZjViODk3NWJkMzA0YTMyYmUzNjdhM2RjNzhkYTViMDRjNDcyZiIsInZlcnNpb24iOjF9.wIjAJNlzzk-M8tsigytlLRYy0uQDGo3Qy1F7afmk5b1XrGnAy1E4Mw-JHDtbZ2uYZiPx0grbOOxL-yT_4DCSCg
    - type: f1
      value: 0.7715000000000001
      name: F1 Micro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTVhZDA4ZWM3YzgzYjVlOTk2ODkxNzQzNjBjMjBlMmZiM2QwN2QyYTVjMDUxNWU3ZTQ2MGZhNGIxYTY3NGI0ZSIsInZlcnNpb24iOjF9.-VYy5OLXwpaoiD4HR7wBjmV5izt2yTXvRbp93cs7jPvPEij7rkidjd-HpVaHMvIOLoTjxnKozFf0pmNQF06WBg
    - type: f1
      value: 0.7732314418938615
      name: F1 Weighted
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNmRmNjUyMjU3ZTljYmViMWRiNDMzODE4YTU3ZjU2YzQ3MDQyZGRhYjBmYzU0Yjk0Yjk3MzVmYjNjM2U5YzFjZCIsInZlcnNpb24iOjF9.BguI5gGX0H4P8LNTAayaBxv7rUYqvepCyKo9rOIsEXsTVN9N-J9IfjUGjptpKJBpOXEi_MGFLV6H7IJUyhdbDA
    - type: precision
      value: 0.7508336175429541
      name: Precision Macro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzM2NDNlZGE2ZDNmYmMyZTU0OWYzOGYwYzM1NWE1YzIzNjBkZjA0NzkxYjY1ZWI4OTM5NWVkZTZkNjgzZTQ1MSIsInZlcnNpb24iOjF9.3YBiMV0HMcEtr4lFDe4BFhTkyfL0EL6Xk3V9ICNOtOMdNgDChRMnphsYh6WaUILJNA0qlmHzh7h_RpciLwMDBw
    - type: precision
      value: 0.7715
      name: Precision Micro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDk5NDk0OGFlNTI1NDhkMTY3NWZmYmYwODBiY2M2YmI0YjkxOWJmYWZiZTViNWQ3ZDk2Mjk3OTNiMDMxMmEwMiIsInZlcnNpb24iOjF9._Zk6Kwarj5Jv_rLX9fp-Np6qwUZwyQ7dD-ylnCJtXEm-ZkarYemTLZqjq_1nWATD3vQcYoHlXD0RFOzYQxSaCw
    - type: precision
      value: 0.7782372190165424
      name: Precision Weighted
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjI5YzIwODcwYjUwMTY1MDVjNThlMGUxMWUzMTQ5MGE5Nzk5ZmZlNTM1ZTQzYjJhNTFkYzkyYzQzOTUwZGRkNiIsInZlcnNpb24iOjF9.OoGtZoogQHq49Vh_MZMO4yASGembVB1xDE216tT_JQGV3zh0_IRdJ9eztxXOn3Hx8qxrQwSEwzKZKp3gj4l3Dw
    - type: recall
      value: 0.7762803886221606
      name: Recall Macro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGI5YTI4OWI5ZGVkOWUyYzc3NzI1M2I1MWUzN2JmOGQ3ODlmN2MwMDI0MmI0ZjkxZWZjNDZjOTNkODg4ZmFlNCIsInZlcnNpb24iOjF9.fkdes7mIwaxI_8AVJuahiZoRq0MZzzMsjDddn8trtxi37fHCMEX86hf__Kmbs5AxrgtkJA3fd4H5iKcEaq1MBA
    - type: recall
      value: 0.7715
      name: Recall Micro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDkwZTQzNzhiNzgyODU1YTVjYzFiMTg4ZGZiYjg5ZTBlYTNkNWM5MWMyZTFkMjQyZDA0OGU3ODUwNzQ0MzNiNiIsInZlcnNpb24iOjF9.JtK5c3OOO9ryDKsddzAykHcj8nF-LvA96oF3MPTqB8FtyWuWQEBJAMhID-xhCgGTfEtD-n_LggDBeww1AZQOBg
    - type: recall
      value: 0.7715
      name: Recall Weighted
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzc5OTY0NWYxNDM5ZDEyMDM2ZDdlYjQ0YWIwMzU2YTQ0YTBjMmE3NGEzOGIzNmY5ODEwNzQ3M2YyOWY3NDVkMCIsInZlcnNpb24iOjF9.gpw2NXq5Z6zj4JXXBDkETnY6dQxKDBLyQP3nGaKeRhTA_sQ7zud0xDiKKSJa8dckE4tSS6fjW-9xoAyvlxFxAw
    - type: loss
      value: 0.525364875793457
      name: loss
      verified: true
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---


# 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).

<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
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
```