Update README.md

README.md

@@ -293,10 +293,10 @@ pipeline_tag: zero-shot-classification
 
 # Model Card for Model ID
 
-deberta-v3-base with context length of 1280 fine-tuned on tasksource for 150k steps. I oversampled long NLI tasks (ConTRoL, doc-nli).
+deberta-v3-base with context length of 1280 fine-tuned on tasksource for 250k steps. I oversampled long NLI tasks (ConTRoL, doc-nli).
 Training data include helpsteer v1/v2, logical reasoning tasks (FOLIO, FOL-nli, LogicNLI...), OASST, hh/rlhf, linguistics oriented NLI tasks, tasksource-dpo, fact verification tasks.
 
-This model is suitable for long context NLI or and as a backbone for reward models or classifiers fine-tuning.
+This model is suitable for long context NLI or as a backbone for reward models or classifiers fine-tuning.
 
 This checkpoint has strong zero-shot validation performance on many tasks (e.g. 70% on WNLI), and can be used for:
 - Zero-shot entailment-based classification for arbitrary labels [ZS].