Update README.md

README.md

@@ -302,13 +302,10 @@ license: apache-2.0
 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 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].
 - Natural language inference [NLI]
-- Hundreds of previous tasks with tasksource-adapters [TA].
-- Further fine-tuning on a new task or tasksource task (classification, token classification or multiple-choice) [FT].
+- Further fine-tuning on a new task or tasksource task (classification, token classification, reward modeling or multiple-choice) [FT].
 
 | dataset                   |   accuracy |
 |:----------------------------|----------------:|