README.md

---
datasets:
- tweet_eval
metrics:
- f1
- accuracy
pipeline_tag: text-classification
widget:
- text: Get the all-analog Classic Vinyl Edition of "Takin Off" Album from {@herbiehancock@}
    via {@bluenoterecords@} link below {{URL}}
  example_title: topic_classification 1
- text: Yes, including Medicare and social security saving👍
  example_title: sentiment 1
- text: All two of them taste like ass.
  example_title: offensive 1
- text: If you wanna look like a badass, have drama on social media
  example_title: irony 1
- text: Whoever just unfollowed me you a bitch
  example_title: hate 1
- text: I love swimming for the same reason I love meditating...the feeling of weightlessness.
  example_title: emotion 1
- text: Beautiful sunset last night from the pontoon @TupperLakeNY
  example_title: emoji 1
model-index:
- name: cardiffnlp/roberta-base-offensive
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: tweet_eval
      type: offensive
      split: test
    metrics:
    - type: micro_f1_tweet_eval/offensive
      value: 0.8441860465116279
      name: Micro F1 (tweet_eval/offensive)
    - type: micro_f1_tweet_eval/offensive
      value: 0.8038468085106383
      name: Macro F1 (tweet_eval/offensive)
    - type: accuracy_tweet_eval/offensive
      value: 0.8441860465116279
      name: Accuracy (tweet_eval/offensive)
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: tweet_eval
      type: tweet_eval
      config: offensive
      split: train
    metrics:
    - type: accuracy
      value: 0.8775595837529372
      name: Accuracy
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTQ3ZjIwZDAyZDc5MmU0ZWE3Mjg3MGZkMzJjYTA4ODYxMmI1NmUyNWUyMWQwYjhhOThiMjVlYzcwMTIyYWE3NiIsInZlcnNpb24iOjF9.FGKrLdRO1Iljnac-g6wty0HrcE5vwfHpWzRtzm-lPKsInyrGbtFC6mh6fpWHquoKZN_XVD-3Ju1ivROv3PsYDA
    - type: f1
      value: 0.8617195443801995
      name: F1 Macro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmE2MTJkNjUzOTczN2NkNzFmZmFkMmVlNzNkMTQxNThmYjY1NDJmYjI2MjJhZjc2M2I1OTJlYjg3ODQ5NTAwYiIsInZlcnNpb24iOjF9.JyPCbdFBiSnKAHO_fpGPKolFfS-QxCmgGILFTtRsO0yr53SFWZzvuWU3LF4eG_EkCskOCkhzJHe9ydFScf1cCg
    - type: f1
      value: 0.8775595837529372
      name: F1 Micro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOWJjZDdiNGQxYTEzOWM1ZWU2NzI5MDkyNmQ2NjNhYWMzZmUyMmU4YWYyMjQ3ZjlkNDFhNGFiZmM0ZjEyMmE0MSIsInZlcnNpb24iOjF9.h4RE_k9PKIV2aoJxt9K_hStetS0jvvnZuumo6EWqZek1jrVdNCw8hecEfpDxCMuV1nJG_Nb1Qb2CPHaehoiaAA
    - type: f1
      value: 0.8775635113600219
      name: F1 Weighted
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjczZjlhOTlmOTYyOGZkODFhMzBkNThjNmQ3ZTVhZTY0ZTQ2YWY3YTY4ZDU1M2E5ZmEyMmFmODI2NjJjMTc5YiIsInZlcnNpb24iOjF9.LNoaYMgzp63FR4pgt49Bi-6Fwb7ocicdGesMntzBV9Y_eNl7f4Jx-Jl1V8jjB-Mas_Fj1BHqYgmVnsokZEnDCA
    - type: precision
      value: 0.8616963464593261
      name: Precision Macro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOWQ2MmI4YTI2NThlMDExODhkYzVhOGY4NmFlNjAxMzNjOTQ2NzNjYTBhYmM2NGRkYzIyYTEwYmQ2MDhhYzc5MyIsInZlcnNpb24iOjF9.dcwR0Y2MUzNt_-YSNFyLzxsVzCAglflGeLEm1EhQ2xU9cOpxKmGOADEETRVN-s8Qo-rfR0UTLBf8s1m_AJ01Bg
    - type: precision
      value: 0.8775595837529372
      name: Precision Micro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjdlN2VhNjllYjNhMDM1NmQwZGVkMTZiZjY1ZjgxNDZlMDRlZjY2NGE2NzkwOTIwMzBlODQ1NTIwOTUzODVhMiIsInZlcnNpb24iOjF9.bMukPZRCgLsH5bRqkUys1DjubnLFh39mj0JEmWkGNPKNqgRq11IDsHpMICK2l8_kW25_wpiThELRXlYWI8L6CA
    - type: precision
      value: 0.8775674524222297
      name: Precision Weighted
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOWU2NDE3NzkxYjFlNDM3ZmI4MzQwYTVjOWVkY2Q3MTIwZGVlZTUzYzBkNGFmMjU4ODVlZTQwYTdlYzBlNDRjNSIsInZlcnNpb24iOjF9.PLj9bhs5wyqcANvgiYVbf8Gnpkn7H1IWg7lUjXez60QxfOcN0LdXbGttxu_y13Q41mbF4RW9MkC_OlVgxgiOBw
    - type: recall
      value: 0.8617427589865883
      name: Recall Macro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzllZDBjMGY2ZTYyYTA5ODc4NjU1NWZkYTM4MWZlMDFkNjJhYTg3MTYwODYyZDYwYzc5MDliMTAzM2Q2NTk4YyIsInZlcnNpb24iOjF9.PUlMOsCQrowlUu1GGR9T2Hd50cOLsQHwu1FuwiLvWB25fLJYjFGTIai0UdBmtlTSKmviye_QzXrX1H_dJUAkBA
    - type: recall
      value: 0.8775595837529372
      name: Recall Micro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjhkMzYxMWNmZjYxYTE2N2JjMjRhOWQ4YjZhMzI3NWU2YWI2ODI2MGViZmE0M2NkYjdmYmRmNTBkMjkwOTVlNiIsInZlcnNpb24iOjF9.PT7NY-polKG346y1T7fq1vC_wtzI_niOFeIuCZqXbexwnmtPKQYZGW8ag3690u4D_8wP9KQlJuPimiiO5OzRBg
    - type: recall
      value: 0.8775595837529372
      name: Recall Weighted
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmY5M2RkZmU2ZDQyNzIzYjA4MGY3MTZmMGViYTU3OWI4ODFlN2VhOWVhYWEwN2VkOWM3YTQ0ODU3NDk5MzNkMSIsInZlcnNpb24iOjF9.U1k9ishrbEKkcceXP-FgodUG-GbE-g1B1tK-hCpZNpCYKicZrxI7Ft5fNZ9jGjO8_eRZNpL8o1DYmON2-kjFBw
    - type: loss
      value: 0.31321173906326294
      name: loss
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNWMwYzM0MTg5YWEzYjBmMTg2NjgwMDc5NDY5NmQ4NWU1MjNjMTE4NzNmMDZmNWQzZGNlZDc3NGZjNzQzZTVjNiIsInZlcnNpb24iOjF9.YGXjIov_YlgdewGVUVySHZwVd874bUxvAkHcNXYf3j_at4DB14V1KLXmts0xXyHz0iTqJPjS6frr0aTHcixvBA
---
# cardiffnlp/roberta-base-offensive 

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the 
[`tweet_eval (offensive)`](https://huggingface.co/datasets/tweet_eval) 
via [`tweetnlp`](https://github.com/cardiffnlp/tweetnlp).
Training split is `train` and parameters have been tuned on the validation split `validation`.

Following metrics are achieved on the test split `test` ([link](https://huggingface.co/cardiffnlp/roberta-base-offensive/raw/main/metric.json)).

- F1 (micro): 0.8441860465116279
- F1 (macro): 0.8038468085106383
- Accuracy: 0.8441860465116279

### Usage
Install tweetnlp via pip.
```shell
pip install tweetnlp
```
Load the model in python.
```python
import tweetnlp
model = tweetnlp.Classifier("cardiffnlp/roberta-base-offensive", max_length=128)
model.predict('Get the all-analog Classic Vinyl Edition of "Takin Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below {{URL}}')
```



### Reference 

 
```
@inproceedings{camacho-collados-etal-2022-tweetnlp,
    title={{T}weet{NLP}: {C}utting-{E}dge {N}atural {L}anguage {P}rocessing for {S}ocial {M}edia},
    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},
    author = "Ushio, Asahi  and
      Camacho-Collados, Jose",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2022",
    address = "Abu Dhabi, U.A.E.",
    publisher = "Association for Computational Linguistics",
}
```