Update README.md

README.md

@@ -16,11 +16,12 @@ The labels explanation:
 - `relevance`: is the last message in the dialogue relevant in the context of the full dialogue
 - `specificity`: is the last message in the dialogue interesting and promotes the continuation of the dialogue
 
-The preferable length of the dialogue is 4 where the last message is needed to be estimated
-
 It is pretrained on a large corpus of dialog data in unsupervised manner: the model is trained to predict whether last response was in a real dialog, or it was pulled from some other dialog at random.
 
 Then it was finetuned on manually labelled examples (dataset will be posted soon).
+
+The model was trained with the dialogue length 4 where the last message is needed to be estimated. Each message in the dialogue was tokenized separately with ```  max_length = max_seq_length // 4 ```.
+
 It is pretrained on corpus of dialog data and finetuned on [tinkoff-ai/context_similarity](https://huggingface.co/tinkoff-ai/context_similarity). 
 The performance of the model on validation split (dataset will be posted soon)[tinkoff-ai/context_similarity](https://huggingface.co/tinkoff-ai/context_similarity) (with the best thresholds for validation samples):
 
@@ -47,5 +48,6 @@ with torch.inference_mode():
 relevance, specificity = probas
 ```
 
-The [app] (https://huggingface.co/spaces/tinkoff-ai/response-quality-classifiers) where you can easily evaluate this model
+The [app](https://huggingface.co/spaces/tinkoff-ai/response-quality-classifiers) where you can easily evaluate this model
+
 The work was done during internship at Tinkoff by [egoriyaa](https://github.com/egoriyaa)