Update README.md

README.md

@@ -1,15 +1,73 @@
 ---
 base_model: readerbench/RoBERT-base
+language:
+- ro
 tags:
-- generated_from_trainer
+- sentiment
+- classification
+- nlp
+- bert
+datasets:
+- decathlon_reviews
+- cinemagia_reviews
 metrics:
 - accuracy
 - precision
 - recall
 - f1
+- f1 weighted
 model-index:
 - name: ro-sentiment-03
-  results: []
+  results: 
+  - task:
+      type: text-classification             # Required. Example: automatic-speech-recognition
+      name: Text Classification             # Optional. Example: Speech Recognition
+    dataset:
+      type: ro_sent          # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
+      name: Rommanian Sentiment Dataset          # Required. A pretty name for the dataset. Example: Common Voice (French)
+      config: default      # Optional. The name of the dataset configuration used in `load_dataset()`. Example: fr in `load_dataset("common_voice", "fr")`. See the `datasets` docs for more info: https://huggingface.co/docs/datasets/package_reference/loading_methods#datasets.load_dataset.name
+      split: all        # Optional. Example: test
+    metrics:
+      - type: accuracy         # Required. Example: wer. Use metric id from https://hf.co/metrics
+        value: 0.85       # Required. Example: 20.90
+        name: Accuracy         # Optional. Example: Test WER
+      - type: precision         # Required. Example: wer. Use metric id from https://hf.co/metrics
+        value: 0.85       # Required. Example: 20.90
+        name: Precision         # Optional. Example: Test WER
+      - type: recall         # Required. Example: wer. Use metric id from https://hf.co/metrics
+        value: 0.85       # Required. Example: 20.90
+        name: Recall         # Optional. Example: Test WER
+      - type: f1_weighted         # Required. Example: wer. Use metric id from https://hf.co/metrics
+        value: 0.85       # Required. Example: 20.90
+        name: Weighted F1         # Optional. Example: Test WER
+      - type: f1_macro         # Required. Example: wer. Use metric id from https://hf.co/metrics
+        value: 0.84       # Required. Example: 20.90
+        name: Weighted F1         # Optional. Example: Test WER
+  - task:
+      type: text-classification             # Required. Example: automatic-speech-recognition
+      name: Text Classification             # Optional. Example: Speech Recognition
+    dataset:
+      type: laroseda          # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
+      name: A Large Romanian Sentiment Data Set          # Required. A pretty name for the dataset. Example: Common Voice (French)
+      config: default      # Optional. The name of the dataset configuration used in `load_dataset()`. Example: fr in `load_dataset("common_voice", "fr")`. See the `datasets` docs for more info: https://huggingface.co/docs/datasets/package_reference/loading_methods#datasets.load_dataset.name
+      split: all        # Optional. Example: test
+    metrics:
+      - type: accuracy         # Required. Example: wer. Use metric id from https://hf.co/metrics
+        value: 0.85       # Required. Example: 20.90
+        name: Accuracy         # Optional. Example: Test WER
+      - type: precision         # Required. Example: wer. Use metric id from https://hf.co/metrics
+        value: 0.86       # Required. Example: 20.90
+        name: Precision         # Optional. Example: Test WER
+      - type: recall         # Required. Example: wer. Use metric id from https://hf.co/metrics
+        value: 0.85       # Required. Example: 20.90
+        name: Recall         # Optional. Example: Test WER
+      - type: f1_weighted         # Required. Example: wer. Use metric id from https://hf.co/metrics
+        value: 0.84       # Required. Example: 20.90
+        name: Weighted F1         # Optional. Example: Test WER
+      - type: f1_macro         # Required. Example: wer. Use metric id from https://hf.co/metrics
+        value: 0.84       # Required. Example: 20.90
+        name: Weighted F1         # Optional. Example: Test WER
+        
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -26,6 +84,11 @@ It achieves the following results on the evaluation set:
 - F1: 0.8652
 - F1 Weighted: 0.8287
 
+### Evaluation on other datasets
+
+**SENT_RO**
+
+
 ## Model description
 
 More information needed
@@ -50,14 +113,14 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_ratio: 0.2
-- num_epochs: 10
+- num_epochs: 10 (Early stop epoch 3, best epoch 2)
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | F1 Weighted |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------:|
 | 0.4198        | 1.0   | 1629 | 0.3983          | 0.8377   | 0.8791    | 0.8721 | 0.8756 | 0.8380      |
-| 0.3861        | 2.0   | 3258 | 0.4312          | 0.8429   | 0.8963    | 0.8665 | 0.8812 | 0.8442      |
+| 0.3861        | **2.0**   | 3258 | 0.4312          | 0.8429   | 0.8963    | 0.8665 | 0.8812 | **0.8442**      |
 | 0.3189        | 3.0   | 4887 | 0.3923          | 0.8307   | 0.8366    | 0.8959 | 0.8652 | 0.8287      |