
# Twitter June 2022 (RoBERTa-base, 132M)

This is a RoBERTa-base model trained on 132.26M tweets until the end of June 2022.
More details and performance scores 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-base-jun2022"
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) 0.36928  not
2) 0.29651  fully
3) 0.15332  getting
4) 0.04144  still
5) 0.01805  all
------------------------------
I keep forgetting to bring a <mask>.
1) 0.06048  book
2) 0.03458  backpack
3) 0.03362  lighter
4) 0.03162  charger
5) 0.02832  pen
------------------------------
Looking forward to watching <mask> Game tonight!
1) 0.65149  the
2) 0.14239  The
3) 0.02432  this
4) 0.00877  End
5) 0.00866  Big
```

## 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-base-jun2022"
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) 0.98882 The movie was great
2) 0.96087 Just finished reading 'Embeddings in NLP'
3) 0.95450 I just ordered fried chicken 🐣
4) 0.95300 What time is the next game?
```

## Example Feature Extraction 

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

MODEL = "cardiffnlp/twitter-roberta-base-jun2022"
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)
```
{"language": "en", "license": "mit", "tags": ["timelms", "twitter"], "datasets": ["twitter-api"]}
transformers pytorch roberta fill-mask timelms twitter en dataset:twitter-api arxiv:2202.03829 license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us 


# Twitter June 2022 (RoBERTa-base, 132M)

This is a RoBERTa-base model trained on 132.26M tweets until the end of June 2022.
More details and performance scores 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 


# Twitter June 2022 (RoBERTa-base, 132M)

This is a RoBERTa-base model trained on 132.26M tweets until the end of June 2022.
More details and performance scores 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
TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # Twitter June 2022 (RoBERTa-base, 132M)

This is a RoBERTa-base model trained on 132.26M tweets until the end of June 2022.
More details and performance scores 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
passage: TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# Twitter June 2022 (RoBERTa-base, 132M)

This is a RoBERTa-base model trained on 132.26M tweets until the end of June 2022.
More details and performance scores 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