
# Twitter 2022 154M (RoBERTa-large, 154M - full update)

This is a RoBERTa-large model trained on 154M tweets until the end of December 2022 (from original checkpoint, no incremental updates). 
A base model trained on the same datais available [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2022-154m).

These 154M tweets result from filtering 220M tweets obtained exclusively from the Twitter Academic API, covering every month between 2018-01 and 2022-12.
Filtering and preprocessing details are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).

Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the [TimeLMs repository](https://github.com/cardiffnlp/timelms).

For other models trained until different periods, check this [table](https://github.com/cardiffnlp/timelms#released-models).

## Preprocess Text 
Replace usernames and links for placeholders: "@user" and "http".
If you're interested in retaining verified users which were also retained during training, you may keep the users listed [here](https://github.com/cardiffnlp/timelms/tree/main/data).
```python
def preprocess(text):
    preprocessed_text = []
    for t in text.split():
        if len(t) > 1:
            t = '@user' if t[0] == '@' and t.count('@') == 1 else t
            t = 'http' if t.startswith('http') else t
        preprocessed_text.append(t)
    return ' '.join(preprocessed_text)
```

## Example Masked Language Model 

```python
from transformers import pipeline, AutoTokenizer

MODEL = "cardiffnlp/twitter-roberta-large-2022-154m"
fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL)
tokenizer = AutoTokenizer.from_pretrained(MODEL)

def pprint(candidates, n):
    for i in range(n):
        token = tokenizer.decode(candidates[i]['token'])
        score = candidates[i]['score']
        print("%d) %.5f %s" % (i+1, score, token))

texts = [
    "So glad I'm <mask> vaccinated.",
    "I keep forgetting to bring a <mask>.",
    "Looking forward to watching <mask> Game tonight!",
]
for text in texts:
    t = preprocess(text)
    print(f"{'-'*30}\n{t}")
    candidates = fill_mask(t)
    pprint(candidates, 5)
```

Output: 

```
------------------------------
So glad I'm <mask> vaccinated.
1) 0.37136  fully
2) 0.20631  a
3) 0.09422  the
4) 0.07649  not
5) 0.04505  already
------------------------------
I keep forgetting to bring a <mask>.
1) 0.10507  mask
2) 0.05810  pen
3) 0.05142  charger
4) 0.04082  tissue
5) 0.03955  lighter
------------------------------
Looking forward to watching <mask> Game tonight!
1) 0.45783  The
2) 0.32842  the
3) 0.02705  Squid
4) 0.01157  Big
5) 0.00538  Match
```

## Example Tweet Embeddings
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np
from scipy.spatial.distance import cosine
from collections import Counter

def get_embedding(text):  # naive approach for demonstration
  text = preprocess(text)
  encoded_input = tokenizer(text, return_tensors='pt')
  features = model(**encoded_input)
  features = features[0].detach().cpu().numpy() 
  return np.mean(features[0], axis=0) 


MODEL = "cardiffnlp/twitter-roberta-large-2022-154m"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModel.from_pretrained(MODEL)

query = "The book was awesome"
tweets = ["I just ordered fried chicken 🐣", 
          "The movie was great",
          "What time is the next game?",
          "Just finished reading 'Embeddings in NLP'"]

sims = Counter()
for tweet in tweets:
    sim = 1 - cosine(get_embedding(query), get_embedding(tweet))
    sims[tweet] = sim

print('Most similar to: ', query)
print(f"{'-'*30}")
for idx, (tweet, sim) in enumerate(sims.most_common()):
    print("%d) %.5f %s" % (idx+1, sim, tweet))
```
Output: 

```
Most similar to:  The book was awesome
------------------------------
1) 0.99820 The movie was great
2) 0.99306 Just finished reading 'Embeddings in NLP'
3) 0.99257 What time is the next game?
4) 0.98561 I just ordered fried chicken 🐣
```

## Example Feature Extraction 

```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np

MODEL = "cardiffnlp/twitter-roberta-large-2022-154m"
tokenizer = AutoTokenizer.from_pretrained(MODEL)

text = "Good night 😊"
text = preprocess(text)

# Pytorch
model = AutoModel.from_pretrained(MODEL)
encoded_input = tokenizer(text, return_tensors='pt')
features = model(**encoded_input)
features = features[0].detach().cpu().numpy() 
features_mean = np.mean(features[0], axis=0) 
#features_max = np.max(features[0], axis=0)

# # Tensorflow
# model = TFAutoModel.from_pretrained(MODEL)
# encoded_input = tokenizer(text, return_tensors='tf')
# features = model(encoded_input)
# features = features[0].numpy()
# features_mean = np.mean(features[0], axis=0) 
# #features_max = np.max(features[0], axis=0)
```

### BibTeX entry and citation info

Please cite the [reference paper](https://arxiv.org/abs/2308.02142) if you use this model.

```bibtex
@article{loureiro2023tweet,
  title={Tweet Insights: A Visualization Platform to Extract Temporal Insights from Twitter},
  author={Loureiro, Daniel and Rezaee, Kiamehr and Riahi, Talayeh and Barbieri, Francesco and Neves, Leonardo and Anke, Luis Espinosa and Camacho-Collados, Jose},
  journal={arXiv preprint arXiv:2308.02142},
  year={2023}
}
```

{"language": "en", "license": "mit", "tags": ["timelms", "twitter"], "datasets": ["twitter-api"]}
transformers pytorch roberta fill-mask timelms twitter en dataset:twitter-api arxiv:2202.03829 arxiv:2308.02142 license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #arxiv-2308.02142 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Twitter 2022 154M (RoBERTa-large, 154M - full update)

This is a RoBERTa-large model trained on 154M tweets until the end of December 2022 (from original checkpoint, no incremental updates). 
A base model trained on the same datais available here.

These 154M tweets result from filtering 220M tweets obtained exclusively from the Twitter Academic API, covering every month between 2018-01 and 2022-12.
Filtering and preprocessing details are available in the TimeLMs paper.

Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.

For other models trained until different periods, check this table.

## Preprocess Text 
Replace usernames and links for placeholders: "@user" and "http".
If you're interested in retaining verified users which were also retained during training, you may keep the users listed here.


## Example Masked Language Model 



Output: 



## Example Tweet Embeddings

Output: 



## Example Feature Extraction 



### BibTeX entry and citation info

Please cite the reference paper if you use this model.



# Twitter 2022 154M (RoBERTa-large, 154M - full update)

This is a RoBERTa-large model trained on 154M tweets until the end of December 2022 (from original checkpoint, no incremental updates). 
A base model trained on the same datais available here.

These 154M tweets result from filtering 220M tweets obtained exclusively from the Twitter Academic API, covering every month between 2018-01 and 2022-12.
Filtering and preprocessing details are available in the TimeLMs paper.

Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.

For other models trained until different periods, check this table. ## Preprocess Text 
Replace usernames and links for placeholders: "@user" and "http".
If you're interested in retaining verified users which were also retained during training, you may keep the users listed here. ## Example Masked Language Model 



Output: ## Example Tweet Embeddings

Output: ## Example Feature Extraction ### BibTeX entry and citation info

Please cite the reference paper if you use this model.
TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #arxiv-2308.02142 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Twitter 2022 154M (RoBERTa-large, 154M - full update)

This is a RoBERTa-large model trained on 154M tweets until the end of December 2022 (from original checkpoint, no incremental updates). 
A base model trained on the same datais available here.

These 154M tweets result from filtering 220M tweets obtained exclusively from the Twitter Academic API, covering every month between 2018-01 and 2022-12.
Filtering and preprocessing details are available in the TimeLMs paper.

Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.

For other models trained until different periods, check this table. ## Preprocess Text 
Replace usernames and links for placeholders: "@user" and "http".
If you're interested in retaining verified users which were also retained during training, you may keep the users listed here. ## Example Masked Language Model 



Output: ## Example Tweet Embeddings

Output: ## Example Feature Extraction ### BibTeX entry and citation info

Please cite the reference paper if you use this model.
TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #arxiv-2308.02142 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Twitter 2022 154M (RoBERTa-large, 154M - full update)

This is a RoBERTa-large model trained on 154M tweets until the end of December 2022 (from original checkpoint, no incremental updates). 
A base model trained on the same datais available here.

These 154M tweets result from filtering 220M tweets obtained exclusively from the Twitter Academic API, covering every month between 2018-01 and 2022-12.
Filtering and preprocessing details are available in the TimeLMs paper.

Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.

For other models trained until different periods, check this table.## Preprocess Text 
Replace usernames and links for placeholders: "@user" and "http".
If you're interested in retaining verified users which were also retained during training, you may keep the users listed here.## Example Masked Language Model 



Output:## Example Tweet Embeddings

Output:## Example Feature Extraction### BibTeX entry and citation info

Please cite the reference paper if you use this model.