cardiffnlp
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tweet-topic-base-multilingual

Text Classification

Safetensors

xlm-roberta

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antypasd commited on Oct 6

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 ---
-library_name: transformers
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-# Model Card for Model ID
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 ---
+language:
+- en
+- es
+- ja
+- el
+widget:
+- text: It is great to see athletes promoting awareness for climate change.
+datasets:
+- cardiffnlp/tweet_topic_multi
+- cardiffnlp/tweet_topic_multilingual
+license: mit
+metrics:
+- f1
+pipeline_tag: text-classification
 ---
+# tweet-topic-base-multilingual
+This model is based on a [cardiffnlp/twitter-xlm-roberta-base](cardiffnlp/twitter-xlm-roberta-base) language model trained rained on ~198M multilingual tweets and finetuned for multi-label topic classification in English, Spanish, Japanese, and Greek.
+The models is trained using [TweetTopic](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi) and [X-Topic](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multilingual) datasets.
+<b>Labels</b>:
+| <span style="font-weight:normal">0: arts_&_culture</span>           | <span style="font-weight:normal">5: fashion_&_style</span>   | <span style="font-weight:normal">10: learning_&_educational</span>  | <span style="font-weight:normal">15: science_&_technology</span>  |
+|-----------------------------|---------------------|----------------------------|--------------------------|
+| 1: business_&_entrepreneurs | 6: film_tv_&_video  | 11: music                  | 16: sports               |
+| 2: celebrity_&_pop_culture  | 7: fitness_&_health | 12: news_&_social_concern  | 17: travel_&_adventure   |
+| 3: diaries_&_daily_life     | 8: food_&_dining    | 13: other_hobbies          | 18: youth_&_student_life |
+| 4: family                   | 9: gaming           | 14: relationships          |                          |
+## Full classification example
+```python
+from transformers import AutoModelForSequenceClassification, TFAutoModelForSequenceClassification
+from transformers import AutoTokenizer
+import numpy as np
+from scipy.special import expit
+MODEL = f"cardiffnlp/tweet-topic-base-multilingual"
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+# PT
+model = AutoModelForSequenceClassification.from_pretrained(MODEL)
+class_mapping = model.config.id2label
+text = "It is great to see athletes promoting awareness for climate change."
+tokens = tokenizer(text, return_tensors='pt')
+output = model(**tokens)
+scores = output[0][0].detach().numpy()
+scores = expit(scores)
+predictions = (scores >= 0.5) * 1
+# TF
+#tf_model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
+#class_mapping = tf_model.config.id2label
+#text = "It is great to see athletes promoting awareness for climate change."
+#tokens = tokenizer(text, return_tensors='tf')
+#output = tf_model(**tokens)
+#scores = output[0][0]
+#scores = expit(scores)
+#predictions = (scores >= 0.5) * 1
+# Map to classes
+for i in range(len(predictions)):
+  if predictions[i]:
+    print(class_mapping[i])
+```
+Output:
+```
+news_&_social_concern
+sports
+```
+## Results on X-Topic
+|       | English | Spanish | Japanese | Greek |
+|--------------|---------|---------|----------|-------|
+| **Macro-F1** | 55.4    | 48.5    | 50.8     | 41.3  |
+| **Micro-F1** | 63.5    | 63.3    | 57.8     | 69.8  |
+## BibTeX entry and citation info
+TBA