Update README.md

README.md

@@ -1,20 +1,55 @@
 ---
 base_model: readerbench/RoBERT-base
 tags:
-- generated_from_trainer
+- hate speech
+- offensive language
+- romanian
+- classification
+- nlp
+- bert
 metrics:
 - accuracy
 - precision
 - recall
+- f1_macro
+- f1_micro
+- f1_weighted
 model-index:
-- name: ro-offense-01
-  results: []
+- name: ro-offense
+  results: 
+  - task:
+      type: text-classification             # Required. Example: automatic-speech-recognition
+      name: Text Classification             # Optional. Example: Speech Recognition
+    dataset:
+      type: readerbench/ro-offense          # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
+      name: Rommanian Offensive Language 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: test        # Optional. Example: test
+    metrics:
+      - type: accuracy         # Required. Example: wer. Use metric id from https://hf.co/metrics
+        value: 0.8190       # Required. Example: 20.90
+        name: Accuracy         # Optional. Example: Test WER
+      - type: precision         # Required. Example: wer. Use metric id from https://hf.co/metrics
+        value: 0.8138       # Required. Example: 20.90
+        name: Precision         # Optional. Example: Test WER
+      - type: recall         # Required. Example: wer. Use metric id from https://hf.co/metrics
+        value: 0.8118       # 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: 0.8189       # Required. Example: 20.90
+        name: Weighted F1         # Optional. Example: Test WER
+      - type: f1_micro        # Required. Example: wer. Use metric id from https://hf.co/metrics
+        value: 0.8190       # Required. Example: 20.90
+        name: Macro F1         # Optional. Example: Test WER
+      - type: f1_macro         # Required. Example: wer. Use metric id from https://hf.co/metrics
+        value: 0.8126       # Required. Example: 20.90
+        name: Macro F1         # Optional. Example: Test WER
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# ro-offense-01
+# RO-Offense
 
 This model is a fine-tuned version of [readerbench/RoBERT-base](https://huggingface.co/readerbench/RoBERT-base) on an unknown dataset.
 It achieves the following results on the evaluation set:
@@ -28,7 +63,10 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+Finetuned Romanian BERT model for offensive classification.
+
+Trained on the [RO-Offense](https://huggingface.co/datasets/readerbench/ro-offense) Dataset 
+
 
 ## Intended uses & limitations
 
@@ -36,7 +74,9 @@ More information needed
 
 ## Training and evaluation data
 
-More information needed
+Trained on the train split of [RO-Offense](https://huggingface.co/datasets/readerbench/ro-offense) Dataset
+
+Evaluated on the test split of [RO-Offense](https://huggingface.co/datasets/readerbench/ro-offense) Dataset 
 
 ## Training procedure
 
@@ -50,7 +90,7 @@ 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 7, best epoch 4)
 
 ### Training results
 
@@ -59,7 +99,7 @@ The following hyperparameters were used during training:
 | No log        | 1.0   | 125  | 0.7789          | 0.7037   | 0.6825    | 0.7000 | 0.6873   | 0.7037   | 0.7132      |
 | No log        | 2.0   | 250  | 0.5170          | 0.8006   | 0.8066    | 0.8016 | 0.7986   | 0.8006   | 0.7971      |
 | No log        | 3.0   | 375  | 0.5139          | 0.8096   | 0.8168    | 0.8237 | 0.8120   | 0.8096   | 0.8047      |
-| 0.6074        | 4.0   | 500  | 0.6180          | 0.8247   | 0.8251    | 0.8187 | 0.8210   | 0.8247   | 0.8233      |
+| 0.6074        | **4.0**   | 500  | 0.6180          | 0.8247   | 0.8251    | 0.8187 | 0.8210   | 0.8247   | **0.8233**      |
 | 0.6074        | 5.0   | 625  | 0.7311          | 0.8096   | 0.8071    | 0.8085 | 0.8064   | 0.8096   | 0.8071      |
 | 0.6074        | 6.0   | 750  | 0.8365          | 0.8101   | 0.8117    | 0.8191 | 0.8105   | 0.8101   | 0.8051      |
 | 0.6074        | 7.0   | 875  | 0.8411          | 0.8232   | 0.8235    | 0.8210 | 0.8207   | 0.8232   | 0.8210      |