
# Twitter December 2021 (RoBERTa-base, 124M)

This is a RoBERTa-base model trained on 123.86M tweets until the end of December 2021.
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-dec2021"
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) 0.33211  fully
2) 0.26205  not
3) 0.22305  getting
4) 0.03790  still
5) 0.01817  all
------------------------------
I keep forgetting to bring a <mask>.
1) 0.04808  mask
2) 0.04628  book
3) 0.03597  lighter
4) 0.03391  pen
5) 0.02982  knife
------------------------------
Looking forward to watching <mask> Game tonight!
1) 0.34191  Squid
2) 0.23768  the
3) 0.15699  The
4) 0.02766  End
5) 0.01233  this
```

## 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-dec2021"
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) 0.99004 The movie was great
2) 0.96320 Just finished reading 'Embeddings in NLP'
3) 0.95858 I just ordered fried chicken 🐣
4) 0.95356 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-dec2021"
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 December 2021 (RoBERTa-base, 124M)

This is a RoBERTa-base model trained on 123.86M tweets until the end of December 2021.
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 December 2021 (RoBERTa-base, 124M)

This is a RoBERTa-base model trained on 123.86M tweets until the end of December 2021.
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 December 2021 (RoBERTa-base, 124M)

This is a RoBERTa-base model trained on 123.86M tweets until the end of December 2021.
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 December 2021 (RoBERTa-base, 124M)

This is a RoBERTa-base model trained on 123.86M tweets until the end of December 2021.
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