Update README.md

README.md

@@ -8,12 +8,53 @@ language:
 pipeline_tag: text-classification
 ---
 
-# Test Results
+# atasoglu/turkish-base-bert-uncased-offenseval2020_tr
 
-||NOT|OFF|
-|:-|:-|:-|
-|Precision|0.92|0.75|
-|Recall|0.94|0.67|
-|F1-Score|0.93|0.71|
+This is a offensive language detection model fine-tuned with [coltekin/offenseval2020_tr](https://huggingface.co/datasets/coltekin/offenseval2020_tr) dataset on [ytu-ce-cosmos/turkish-base-bert-uncased](https://huggingface.co/ytu-ce-cosmos/turkish-base-bert-uncased).
 
-- Accuracy: 0.89
+## Usage
+
+Quick usage:
+
+```py
+from transformers import pipeline
+pipe = pipeline("text-classification", "atasoglu/turkish-base-bert-uncased-offenseval2020_tr")
+print(pipe("bu bir test metnidir.", top_k=None))
+# [{'label': 'NOT', 'score': 0.9970345497131348}, {'label': 'OFF', 'score': 0.0029654440004378557}]
+```
+
+Or:
+
+```py
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model_id = "atasoglu/turkish-base-bert-uncased-offenseval2020_tr"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForSequenceClassification.from_pretrained(model_id).to(device)
+
+@torch.no_grad
+def predict(X):
+    inputs = tokenizer(X, padding="max_length", truncation=True, max_length=256, return_tensors="pt")
+    outputs = model.forward(**inputs.to(device))
+    return torch.argmax(outputs.logits, dim=-1).tolist()
+
+print(predict(["bu bir test metnidir."]))
+# [0]
+```
+
+## Test Results
+
+Test results examined on the *test* split of fine-tuning dataset.
+
+|             |precision|recall|f1-score|support|
+|------------:|:--------|:-----|:-------|:------|
+|          NOT|0.9162   |0.9559|0.9356  |2812   |
+|          OFF|0.7912   |0.6564|0.7176  |716    |
+
+|             |         |      |        |       |
+|------------:|:--------|:-----|:-------|:------|
+|     accuracy|         |      |0.8951  |3528   |
+|    macro avg|0.8537   |0.8062|0.8266  |3528   |
+| weighted avg|0.8908   |0.8951|0.8914  |3528   |