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README.md

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+---
+datasets:
+- cardiffnlp/tweet_topic_multi
+metrics:
+- f1
+- accuracy
+model-index:
+- name: twitter-roberta-base-dec2020-tweet-topic-multi-all
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: cardiffnlp/tweet_topic_multi
+      type: cardiffnlp/tweet_topic_multi
+      args: cardiffnlp/tweet_topic_multi
+      split: test_2021 
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.7599173553719007
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.5990098728991452
+    - name: Accuracy
+      type: accuracy
+      value: 0.5360333531864205
+pipeline_tag: text-classification
+widget:
+- text: "I'm sure the {@Tampa Bay Lightning@} would’ve rather faced the Flyers but man does their experience versus the Blue Jackets this year and last help them a lot versus this Islanders team. Another meat grinder upcoming for the good guys"
+  example_title: "Example 1"
+- text: "Love to take night time bike rides at the jersey shore. Seaside Heights boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in the US." 
+  example_title: "Example 2"
+---
+# twitter-roberta-base-dec2020-tweet-topic-multi-all
+
+This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020) on the [tweet_topic_multi](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi). This model is fine-tuned on `train_all` split and validated on `test_2021` split of tweet_topic.
+Fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi/blob/main/lm_finetuning.py). It achieves the following results on the test_2021 set:
+
+- F1 (micro): 0.7599173553719007
+- F1 (macro): 0.5990098728991452
+- Accuracy: 0.5360333531864205
+
+
+### Usage
+
+```python
+import math
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+def sigmoid(x):
+  return 1 / (1 + math.exp(-x))
+  
+tokenizer = AutoTokenizer.from_pretrained(twitter-roberta-base-dec2020-tweet-topic-multi-all)
+model = AutoModelForSequenceClassification.from_pretrained(twitter-roberta-base-dec2020-tweet-topic-multi-all, problem_type="multi_label_classification")
+model.eval()
+class_mapping = model.config.id2label
+
+with torch.no_grad():
+  text = #NewVideo Cray Dollas- Water- Ft. Charlie Rose- (Official Music Video)- {{URL}} via {@YouTube@} #watchandlearn {{USERNAME}}
+  tokens = tokenizer(text, return_tensors='pt')
+  output = model(**tokens)
+  flags = [sigmoid(s) > 0.5 for s in output[0][0].detach().tolist()]
+  topic = [class_mapping[n] for n, i in enumerate(flags) if i]
+print(topic)
+```
+
+### Reference
+If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
+
+```
+
+@inproceedings{dimosthenis-etal-2022-twitter,
+    title = "{T}witter {T}opic {C}lassification",
+    author = "Antypas, Dimosthenis  and
+    Ushio, Asahi  and
+    Camacho-Collados, Jose  and
+    Neves, Leonardo  and
+    Silva, Vitor  and
+    Barbieri, Francesco",
+    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
+    month = oct,
+    year = "2022",
+    address = "Gyeongju, Republic of Korea",
+    publisher = "International Committee on Computational Linguistics"
+}
+
+```

metric_summary.json

@@ -1 +1 @@
-{"test/eval_loss": 0.1078503206372261, "test/eval_f1": 0.7599173553719007, "test/eval_f1_macro": 0.5990098728991452, "test/eval_accuracy": 0.5360333531864205, "test/eval_runtime": 73.6316, "test/eval_samples_per_second": 22.803, "test/eval_steps_per_second": 1.426}
+{"test/eval_loss": 0.1078503206372261, "test/eval_f1": 0.7599173553719007, "test/eval_f1_macro": 0.5990098728991452, "test/eval_accuracy": 0.5360333531864205, "test/eval_runtime": 53.3062, "test/eval_samples_per_second": 31.497, "test/eval_steps_per_second": 1.97}