model update

README.md

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+---
+datasets:
+- tweet_eval
+metrics:
+- f1
+- accuracy
+model-index:
+- name: cardiffnlp/roberta-base-sentiment
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: tweet_eval
+      type: sentiment
+      split: test 
+    metrics:
+    - name: Micro F1 (tweet_eval/sentiment)
+      type: micro_f1_tweet_eval/sentiment
+      value: 0.7086453923803321
+    - name: Macro F1 (tweet_eval/sentiment)
+      type: micro_f1_tweet_eval/sentiment
+      value: 0.7097736527692039
+    - name: Accuracy (tweet_eval/sentiment)
+      type: accuracy_tweet_eval/sentiment
+      value: 0.7086453923803321
+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" 
+---
+# cardiffnlp/roberta-base-sentiment 
+
+This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the 
+[`tweet_eval (sentiment)`](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/roberta-base-sentiment/raw/main/metric.json)).
+
+- F1 (micro): 0.7086453923803321
+- F1 (macro): 0.7097736527692039
+- Accuracy: 0.7086453923803321
+
+### Usage
+Install tweetnlp via pip.
+```shell
+pip install tweetnlp
+```
+Load the model in python.
+```python
+import tweetnlp
+model = tweetnlp.Classifier("cardiffnlp/roberta-base-sentiment", 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"
+}
+```
+
+

best_run_hyperparameters.json

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+{"learning_rate": 1.717762111233842e-05, "num_train_epochs": 2, "per_device_train_batch_size": 16}

metric.json

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+{"eval_loss": 0.7201297879219055, "eval_f1": 0.7086453923803321, "eval_f1_macro": 0.7097736527692039, "eval_accuracy": 0.7086453923803321, "eval_runtime": 68.9465, "eval_samples_per_second": 178.167, "eval_steps_per_second": 22.278}