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/twitter-roberta-base-2021-124m-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: 0.858139534883721
      name: Micro F1 (tweet_eval/offensive)
    - type: micro_f1_tweet_eval/offensive
      value: 0.8232706055154664
      name: Macro F1 (tweet_eval/offensive)
    - type: accuracy_tweet_eval/offensive
      value: 0.858139534883721
      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: 0.8682443773078214
      name: Accuracy
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGJkNTljNjU4ZmFmYjVlN2M5YjMxOTEyMzhhYzA0OWYzM2U1MWVlOGQ1ZTJmNGY3YTNjODNmYzgxZjA3NGE4NiIsInZlcnNpb24iOjF9.NY_hmncAf9F010pQucmbKhIWhVWfyGpcUNyavHA53tYC-sGQOzQde8sifPOGTVFq2u7n7YWEYM_6OUHY8Z2RAQ
    - type: f1
      value: 0.8532408201027345
      name: F1 Macro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzdlNjFiOWMzZTdkYmFmOWQ1ODU2YWU5OTg3YzQwZjY1ODdhYjE3ZDc0ODUxZDZiM2FmZDY4NzA1MzY3MGUxZSIsInZlcnNpb24iOjF9.8sqfFWNsda-hnnYSvHJQCJczkarfPftYaCKpYxIC3RFAc3XRCdpVvEBqOzhsJ3m2kgCVVdHXrg8WbSEDh9mEBA
    - type: f1
      value: 0.8682443773078214
      name: F1 Micro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMmExOWFhODliM2IyMDU0Y2U1ZTZiOTM2MWM5MWExNGRjYjMwMmE0MmEyNDRjOTgxNDEwN2Q1M2I5Yjg4OWZhMyIsInZlcnNpb24iOjF9.KKiMpInK2uYzTZT7ECGwpF8uR9rEfi3NO-NySGhs42IVGDVekXlg6LB7JdMR7OVN5Hpj6KTAtX3B9Ku_0sMOCg
    - type: f1
      value: 0.8691264762091178
      name: F1 Weighted
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjdlYWQ4N2NkODJmY2ZkZTNiZjgzOTIwMTMyYzg1MjgwOTc3Y2ZmMGE3NTQ1NGM4ODhhMTc5YTQzODVhMGRlNyIsInZlcnNpb24iOjF9.-CIWG5eSbCdonG6iIIKuzLFEfV2-zx7eNjNtagaKf0zccn0hgLN5pLgwwZQnieUb8cjLE5EjDtV6HUJbh_qBBw
    - type: precision
      value: 0.8489031737065875
      name: Precision Macro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTE1YmFiMDFhNjQyM2Q4YTYwZWU0NGU5MzAzYmU3NDBkODQzOGE1ODkwYWJkN2E5MzJmMDQxNTU5OTEwOWIzMyIsInZlcnNpb24iOjF9.8022ApT081k_TZGFFL1zdpZBQE6BXNDMLtkcLQBp1nkiyECh3AhHMw1y-YPO5zmMVwMwhjY7L0CjJZIye6ZcCw
    - type: precision
      value: 0.8682443773078214
      name: Precision Micro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTEyZDFlNWIyNzIzYWRkMzk1OTEzNGU3NDNiYWFlNTQyODJjNTc4YTMyYmEyZDdhZGJjY2E0MWVkY2Q1MWRmOCIsInZlcnNpb24iOjF9.GSHQgLs8ybqBHQHFinysfDnQmg1IZfbAsZP5fNDLIGi9F7uDhwyp2bovVRtcc7P5zsHKt0SvC1wOpZV4qL6RBg
    - type: precision
      value: 0.8706606793751257
      name: Precision Weighted
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmQyOTc3Y2IzNzNhYjczZWMyNGRjM2VlMTYyYzJlZWYyMWFlOWJlNjg4NjYyM2RkMGU4MTZhNjRiMGNmMDhjMCIsInZlcnNpb24iOjF9.ETcK2QE85cr6gUzQjuZAhZSitf7Q71bko0YgxejPRblaDgoFBIIDgvnwr2P0570Z_aY2phqLJVY4H0uWaQw3DA
    - type: recall
      value: 0.8583773352879741
      name: Recall Macro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMmJlYjVmYzg4OGEzNmFlYTVmNTAyMzEwNGQwMWYxNTNiOWY5ODRmZmUyYzZiODVlY2M2MGI5MDQ5NTNkNTA5MiIsInZlcnNpb24iOjF9.sSwm_LY3NFoisGUZntYDtRCZ6Qmi6cl1018LUgugDtIQBT2sOk_QyotICk4wuzrG6Z6K2MWYVrT7vP-vTEpfBA
    - type: recall
      value: 0.8682443773078214
      name: Recall Micro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTNlNjBmMjc4MmI4YzNiOWQ2ZWUxNTJkN2Q0MDlmYWZjOGQ0NDI0Zjk0ZmNlYzM5Nzk3YzE1OWM5MGMxY2NlOSIsInZlcnNpb24iOjF9.Yb-IGL2iR1vuEbpN3dFDTnsh1w3yTsL5oP4WGnqmj01knATG45MJYtPyC_eKIGR20HaW9iR_aVCW8GE2TpZUAg
    - type: recall
      value: 0.8682443773078214
      name: Recall Weighted
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGM2YmMxZGM0MmZiMzc1MmM3MTdjN2Q0ZGY0OTUzM2IzOWJhYjViNTI0YjU4NmQ3YTQzOTllODUzNjZiNzA2OCIsInZlcnNpb24iOjF9.tFJUmy4JxCO7R6tCalkA1T0251hrCB6GUUUt7WmeCDRRFfkZe8GLXqhnW1gBvgv0g5cGeNHw6uYwOh4hpa6DCA
    - type: loss
      value: 0.33106717467308044
      name: loss
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmIwODg3MzA0NWY1YTAwZjk5ZThhNzM2MzcxMTI1ZjdiOWM1ZTNlODMwZGFmZDMyYjY0NjA0NjRiNzhiNjEwZSIsInZlcnNpb24iOjF9.Habwn92oQOuGBmw_K5hZamuJItEz-XeKhL4Q3PJJyL_9L5xbD2FKGXaYHcjalJFqZRqwQbsQ8JqEjErxqQPKDg
---
# cardiffnlp/twitter-roberta-base-2021-124m-offensive 

This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2021-124m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m) 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/twitter-roberta-base-2021-124m-offensive/raw/main/metric.json)).

- F1 (micro): 0.858139534883721
- F1 (macro): 0.8232706055154664
- Accuracy: 0.858139534883721

### Usage
Install tweetnlp via pip.
```shell
pip install tweetnlp
```
Load the model in python.
```python
import tweetnlp
model = tweetnlp.Classifier("cardiffnlp/twitter-roberta-base-2021-124m-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",
}
```