add model

README.md

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+---
+datasets:
+- irony
+metrics:
+- f1
+- accuracy
+model-index:
+- name: cardiffnlp/twitter-roberta-base-dec2021-irony
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: irony
+      type: tweet_eval
+      split: test 
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.7959183673469388
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.791350632069195
+    - name: Accuracy
+      type: accuracy
+      value: 0.7959183673469388
+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: "Example"
+---
+# cardiffnlp/twitter-roberta-base-dec2021-irony 
+
+This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021) on the 
+[`irony (tweet_eval))`](https://huggingface.co/datasets/irony) 
+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-dec2021-irony/raw/main/metric.json)).
+
+- F1 (micro): 0.7959183673469388
+- F1 (macro): 0.791350632069195
+- Accuracy: 0.7959183673469388
+
+### Usage
+Install tweetnlp via pip.
+```shell
+pip install tweetnlp
+```
+Load the model in python.
+```python
+import tweetnlp
+model = tweetnlp.Classifier(cardiffnlp/twitter-roberta-base-dec2021-irony, 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{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.json

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+{"eval_loss": 1.3228046894073486, "eval_f1": 0.7959183673469388, "eval_f1_macro": 0.791350632069195, "eval_accuracy": 0.7959183673469388, "eval_runtime": 2.2267, "eval_samples_per_second": 352.084, "eval_steps_per_second": 44.01}

pytorch_model.bin

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