Update README.md

README.md

@@ -1,47 +1,42 @@
 ---
-tags:
-- generated_from_keras_callback
 model-index:
 - name: twitter-roberta-base-hate-latest
   results: []
+pipeline_tag: text-classification
 ---
-
-<!-- This model card has been generated automatically according to the information Keras had access to. You should
-probably proofread and complete it, then remove this comment. -->
-
-# twitter-roberta-base-hate-latest
-
-This model was trained from scratch on an unknown dataset.
-It achieves the following results on the evaluation set:
-
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- optimizer: None
-- training_precision: float32
-
-### Training results
-
-
-
-### Framework versions
-
-- Transformers 4.21.2
-- TensorFlow 2.10.0
-- Datasets 2.9.0
-- Tokenizers 0.12.1
+# cardiffnlp/twitter-xlm-roberta-base-hate-spanish
+
+This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2022-154m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2022-154m) for binary hate-speech classification. A combination of 13 different hate-speech datasets in the English language were used to fine-tune the model.
+
+## Following metrics are achieved 
+| **Dataset**                                                                                                                                          | **Accuracy** | **Macro-F1** | **Weighted-F1** |
+|------------------------------------------------------------------------------------------------------------------------------------------------------|:------------:|:------------:|:---------------:|
+| hatEval, SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter                                          |    0.5848    |    0.5657    |      0.5514     |
+| ucberkeley-dlab/measuring-hate-speech                                                                                                                |    0.8706    |    0.8531    |      0.8701     |
+| Detecting East Asian Prejudice on Social Media                                                                                                       |    0.9276    |    0.8935    |      0.9273     |
+| Call me sexist, but                                                                                                                                  |    0.9033    |    0.6288    |      0.8852     |
+| Predicting the Type and Target of Offensive Posts in Social Media                                                                                    |    0.9075    |    0.5984    |      0.8935     |
+| HateXplain                                                                                                                                           |    0.9594    |    0.8024    |      0.9600     |
+| Large Scale Crowdsourcing and Characterization of Twitter Abusive BehaviorLarge Scale Crowdsourcing and Characterization of Twitter Abusive Behavior |    0.6817    |    0.5939    |      0.6233     |
+| Twitter Sentiment Analysis                                                                                                                           |    0.9808    |    0.9258    |      0.9807     |
+| Overview of the HASOC track at FIRE 2019:Hate Speech and Offensive Content Identification in Indo-European Languages                                 |    0.8665    |    0.5562    |      0.8343     |
+| Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter                                                          |    0.9465    |    0.8557    |      0.9440     |
+| Automated Hate Speech Detection and the Problem of Offensive Language                                                                                |    0.9116    |    0.8797    |      0.9100     |
+| Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter                                                          |    0.8378    |    0.8338    |      0.8385     |
+| Multilingual and Multi-Aspect Hate Speech Analysis                                                                                                   |    0.9655    |    0.4912    |      0.9824     |
+| **Overall**                                                                                                                                          |  **0.8827**  |  **0.8383**  |    **0.8842**   |
+
+
+### Usage
+Install tweetnlp via pip.
+```shell
+pip install tweetnlp
+```
+Load the model in python.
+```python
+import tweetnlp
+model = tweetnlp.Classifier("cardiffnlp/twitter-roberta-base-hate-latest")
+model.predict('I love everybody :)')
+>> {'label': 'NOT-HATE'}
+
+```