Update README.md

README.md

@@ -1,3 +1,82 @@
 ---
 license: cc-by-sa-4.0
+datasets:
+- cjvt/sentinews
+language:
+- sl
+library_name: transformers
+pipeline_tag: text-classification
+model-index:
+  - name: sloberta-sentinews-sentence
+    results:
+    - task:
+        type: text-classification
+        name: Sentiment classification
+      dataset:
+        type: cjvt/sentinews
+        name: SentiNews
+        config: sentence_level
+      metrics:
+        - type: f1
+          value: 0.6934678744913757
+          name: Validation macro F1
+      
 ---
+
+# sloberta-sentinews-sentence
+
+Slovenian 3-class sentiment classifier - [SloBERTa](https://huggingface.co/EMBEDDIA/sloberta) fine-tuned on the sentence-level config of the 
+SentiNews dataset.
+
+The model is intended as:  
+(1) an out-of-the box sentence-level sentiment classifier or   
+(2) a sentence-level sentiment classification baseline.
+
+## Fine-tuning details
+The model was fine-tuned on a random 90%/5%/5% train-val-test split of the `sentence_level` configuration of the [cjvt/sentinews](https://huggingface.co/datasets/cjvt/sentinews) dataset 
+using the following hyperparameters:  
+```
+max_length = 79  # 99th percentile of encoded training sequences, sequences are padded/truncated to this length
+batch_size = 128
+optimizer = "adamw_torch"
+learning_rate = 2e-5
+num_epochs = 10
+validation_metric = "macro_f1"
+```
+
+Feel free to inspect `training_args.bin` for more details.
+
+If you wish to directly compare your model to this one, you should use the same split as this model. To do so, use the following code:  
+```python
+import json
+import datasets
+
+# You can find split_indices.json in the 'Files and versions' tab 
+with open("split_indices.json", "r") as f_split:
+  split = json.load(f_split)
+
+data = datasets.load_dataset("cjvt/sentinews", "sentence_level", split="train")
+train_data = data.select(split["train_indices"])
+dev_data = data.select(split["dev_indices"])
+test_data = data.select(split["test_indices"])
+```
+
+## Evaluation results
+
+Best validation results:
+```
+{
+  "eval_accuracy": 0.7207815275310835,
+  "eval_f1_macro": 0.6934678744913757,
+  "eval_f1_negative": 0.7042136003337507,
+  "eval_f1_neutral": 0.759215853398679,
+  "eval_f1_positive": 0.6169741697416974,
+  "eval_loss": 0.6337869167327881,
+  "eval_precision_negative": 0.6685148514851486,
+  "eval_precision_neutral": 0.7752393385552655,
+  "eval_precision_positive": 0.6314199395770392,
+  "eval_recall_negative": 0.74394006170119,
+  "eval_recall_neutral": 0.7438413361169103,
+  "eval_recall_positive": 0.6031746031746031
+}
+```