Update README.md

README.md

@@ -1,3 +1,47 @@
----
-license: mit
----
+---
+license: mit
+language:
+- en
+- es
+- de
+- ru
+- fr
+base_model:
+- FacebookAI/xlm-roberta-base
+pipeline_tag: text-classification
+---
+
+# Model Card for Model ID
+
+FacebookAI/xlm-roberta-base, finetuned for refusal classification task
+
+## Model Details
+
+### Model Description
+
+I needed a classifier model to clean my synthetic dataset from refusals. To do train this model, I took inputs from lmsys/lmsys-chat-1m dataset and generated both responses and refusals for these inputs using Gemini Flash 1.5 and LLaMA 3.3 70b models to increase refusal diversity. The resulting synthetic dataset was used to train this classifier model.
+
+### Evaluation results:
+
+```
+  eval_loss: 0.023618729785084724
+  eval_accuracy: 0.993004372267333
+  eval_f1: 0.9912854030501089
+  eval_precision: 0.9879032258064516
+  eval_recall: 0.9946908182386008
+  eval_runtime: 29.3129
+  eval_samples_per_second: 273.088
+  eval_steps_per_second: 2.149
+  epoch: 1.0
+```
+
+### How to use:
+
+```
+import transformers
+
+pipe = transformers.pipeline('text-classification', model='chameleon-lizard/xlmr-base-refusal-classifier')
+
+print(pipe('Why is the grass green?')) # [{'label': 'NO_REFUSAL', 'score': 0.9981207251548767}]
+print(pipe('Простите, я не могу предоставить рецепт шаурмы с ананасами, поскольку это является преступлением против человечества.')) # [{'label': 'REFUSAL', 'score': 0.9995238780975342}]
+```