model update

README.md

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+---
+datasets:
+- cardiffnlp/tweet_topic_single
+metrics:
+- f1
+- accuracy
+model-index:
+- name: cardiffnlp/roberta-base-topic-single
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: cardiffnlp/tweet_topic_single
+      type: cardiffnlp/tweet_topic_single
+      split: test_2021 
+    metrics:
+    - name: Micro F1 (cardiffnlp/tweet_topic_single)
+      type: micro_f1_cardiffnlp/tweet_topic_single
+      value: 0.8818665091553456
+    - name: Macro F1 (cardiffnlp/tweet_topic_single)
+      type: micro_f1_cardiffnlp/tweet_topic_single
+      value: 0.7359303318518903
+    - name: Accuracy (cardiffnlp/tweet_topic_single)
+      type: accuracy_cardiffnlp/tweet_topic_single
+      value: 0.8818665091553456
+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-topic-single 
+
+This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the 
+[`cardiffnlp/tweet_topic_single`](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single) 
+via [`tweetnlp`](https://github.com/cardiffnlp/tweetnlp).
+Training split is `train_all` and parameters have been tuned on the validation split `validation_2021`.
+
+Following metrics are achieved on the test split `test_2021` ([link](https://huggingface.co/cardiffnlp/roberta-base-topic-single/raw/main/metric.json)).
+
+- F1 (micro): 0.8818665091553456
+- F1 (macro): 0.7359303318518903
+- Accuracy: 0.8818665091553456
+
+### Usage
+Install tweetnlp via pip.
+```shell
+pip install tweetnlp
+```
+Load the model in python.
+```python
+import tweetnlp
+model = tweetnlp.Classifier("cardiffnlp/roberta-base-topic-single", 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{camacho-collados-etal-2022-tweetnlp,
+    title={{T}weet{NLP}: {C}utting-{E}dge {N}atural {L}anguage {P}rocessing for {S}ocial {M}edia},
+    author={Camacho-Collados, Jose and Rezaee, Kiamehr and Riahi, Talayeh and Ushio, Asahi and Loureiro, Daniel and Antypas, Dimosthenis and Boisson, Joanne and Espinosa-Anke, Luis and Liu, Fangyu and Mart{'\i}nez-C{'a}mara, Eugenio and others},
+    author = "Ushio, Asahi  and
+      Camacho-Collados, Jose",
+    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
+    month = nov,
+    year = "2022",
+    address = "Abu Dhabi, U.A.E.",
+    publisher = "Association for Computational Linguistics",
+}
+```
+
+

best_run_hyperparameters.json

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+{"learning_rate": 8.16845589476017e-06, "num_train_epochs": 3, "per_device_train_batch_size": 32}

metric.json

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+{"eval_loss": 0.4330822229385376, "eval_f1": 0.8818665091553456, "eval_f1_macro": 0.7359303318518903, "eval_accuracy": 0.8818665091553456, "eval_runtime": 9.8614, "eval_samples_per_second": 171.68, "eval_steps_per_second": 21.498}