|
--- |
|
language: en |
|
widget: |
|
- text: Covid cases are increasing fast! |
|
datasets: |
|
- tweet_eval |
|
--- |
|
|
|
|
|
# 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 |
|
``` |
|
|
|
|
|
### 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" |
|
} |
|
|
|
``` |
|
|
