Updated Readme

README.md

@@ -277,37 +277,6 @@ datasets:
 - tasksource/tracie
 - tasksource/sherliic
 - tasksource/sen-making
-- tasksource/winowhy
-- mediabiasgroup/mbib-base
-- tasksource/robustLR
-- CLUTRR/v1
-- tasksource/logical-fallacy
-- tasksource/parade
-- tasksource/cladder
-- tasksource/subjectivity
-- tasksource/MOH
-- tasksource/VUAC
-- tasksource/TroFi
-- sharc_modified
-- tasksource/conceptrules_v2
-- tasksource/disrpt
-- conll2000
-- DFKI-SLT/few-nerd
-- tasksource/com2sense
-- tasksource/scone
-- tasksource/winodict
-- tasksource/fool-me-twice
-- tasksource/monli
-- tasksource/corr2cause
-- tasksource/apt
-- zeroshot/twitter-financial-news-sentiment
-- tasksource/icl-symbol-tuning-instruct
-- tasksource/SpaceNLI
-- sihaochen/propsegment
-- HannahRoseKirk/HatemojiBuild
-- tasksource/regset
-- tasksource/babi_nli
-- lmsys/chatbot_arena_conversations
 metrics:
 - accuracy
 library_name: transformers
@@ -325,25 +294,12 @@ This checkpoint has strong zero-shot validation performance on many tasks (e.g.
 # [ZS] Zero-shot classification pipeline
 ```python
 from transformers import pipeline
-classifier = pipeline("zero-shot-classification",model="sileod/deberta-v3-base-tasksource-nli")
+classifier = pipeline("zero-shot-classification",model="Azma-AI/deberta-base-multi-label-classifier")
 
 text = "one day I will see the world"
 candidate_labels = ['travel', 'cooking', 'dancing']
 classifier(text, candidate_labels)
 ```
-NLI training data of this model includes [label-nli](https://huggingface.co/datasets/tasksource/zero-shot-label-nli), a NLI dataset specially constructed to improve this kind of zero-shot classification.
-
-# [TA] Tasksource-adapters: 1 line access to hundreds of tasks 
-
-```python
-!pip install tasknet tasksource
-import tasknet as tn
-pipe = tn.load_pipeline('sileod/deberta-v3-base-tasksource-nli','glue/sst2') # works for 500+ tasksource tasks
-pipe(['That movie was great !', 'Awful movie.'])
-# [{'label': 'positive', 'score': 0.9956}, {'label': 'negative', 'score': 0.9967}]
-```
-The list of tasks is available in model config.json.
-This is more efficient than ZS since it requires only one forward pass per example, but it is less flexible.
 
 
 ## Evaluation