diff --git a/flaml/automl.py b/flaml/automl.py
index c68c7e0fa..14617a6fe 100644
--- a/flaml/automl.py
+++ b/flaml/automl.py
@@ -2322,7 +2322,7 @@ class AutoML(BaseEstimator):
         try:
             from ray import __version__ as ray_version
 
-            assert ray_version >= "1.0.0"
+            assert ray_version >= "1.10.0"
             import ray
             from ray.tune.suggest import ConcurrencyLimiter
         except (ImportError, AssertionError):
@@ -2341,7 +2341,7 @@ class AutoML(BaseEstimator):
             try:
                 from ray import __version__ as ray_version
 
-                assert ray_version >= "1.0.0"
+                assert ray_version >= "1.10.0"
                 from ray.tune.suggest.optuna import OptunaSearch as SearchAlgo
             except (ImportError, AssertionError):
                 from .searcher.suggestion import OptunaSearch as SearchAlgo
@@ -2510,7 +2510,7 @@ class AutoML(BaseEstimator):
         try:
             from ray import __version__ as ray_version
 
-            assert ray_version >= "1.0.0"
+            assert ray_version >= "1.10.0"
             from ray.tune.suggest import ConcurrencyLimiter
         except (ImportError, AssertionError):
             from .searcher.suggestion import ConcurrencyLimiter
@@ -2520,7 +2520,7 @@ class AutoML(BaseEstimator):
             try:
                 from ray import __version__ as ray_version
 
-                assert ray_version >= "1.0.0"
+                assert ray_version >= "1.10.0"
                 from ray.tune.suggest.optuna import OptunaSearch as SearchAlgo
             except (ImportError, AssertionError):
                 from .searcher.suggestion import OptunaSearch as SearchAlgo
diff --git a/flaml/searcher/blendsearch.py b/flaml/searcher/blendsearch.py
index b166691ed..da1913c5b 100644
--- a/flaml/searcher/blendsearch.py
+++ b/flaml/searcher/blendsearch.py
@@ -11,7 +11,7 @@ import pickle
 try:
     from ray import __version__ as ray_version
 
-    assert ray_version >= "1.0.0"
+    assert ray_version >= "1.10.0"
     from ray.tune.suggest import Searcher
     from ray.tune.suggest.optuna import OptunaSearch as GlobalSearch
 except (ImportError, AssertionError):
@@ -856,7 +856,7 @@ class BlendSearch(Searcher):
 try:
     from ray import __version__ as ray_version
 
-    assert ray_version >= "1.0.0"
+    assert ray_version >= "1.10.0"
     from ray.tune import (
         uniform,
         quniform,
diff --git a/flaml/searcher/search_thread.py b/flaml/searcher/search_thread.py
index a148256b8..fec85a7d3 100644
--- a/flaml/searcher/search_thread.py
+++ b/flaml/searcher/search_thread.py
@@ -8,7 +8,7 @@ import numpy as np
 try:
     from ray import __version__ as ray_version
 
-    assert ray_version >= "1.0.0"
+    assert ray_version >= "1.10.0"
     from ray.tune.suggest import Searcher
 except (ImportError, AssertionError):
     from .suggestion import Searcher
diff --git a/flaml/tune/__init__.py b/flaml/tune/__init__.py
index 755159339..7c441c32a 100644
--- a/flaml/tune/__init__.py
+++ b/flaml/tune/__init__.py
@@ -1,7 +1,7 @@
 try:
     from ray import __version__ as ray_version
 
-    assert ray_version >= "1.0.0"
+    assert ray_version >= "1.10.0"
     from ray.tune import (
         uniform,
         quniform,
diff --git a/flaml/tune/space.py b/flaml/tune/space.py
index fbca06a1b..3f44d36f2 100644
--- a/flaml/tune/space.py
+++ b/flaml/tune/space.py
@@ -1,7 +1,7 @@
 try:
     from ray import __version__ as ray_version
 
-    assert ray_version >= "1.0.0"
+    assert ray_version >= "1.10.0"
     from ray.tune import sample
     from ray.tune.suggest.variant_generator import generate_variants
 except (ImportError, AssertionError):
diff --git a/flaml/tune/tune.py b/flaml/tune/tune.py
index c6959dc39..b77d1eb62 100644
--- a/flaml/tune/tune.py
+++ b/flaml/tune/tune.py
@@ -10,7 +10,7 @@ import time
 try:
     from ray import __version__ as ray_version
 
-    assert ray_version >= "1.0.0"
+    assert ray_version >= "1.10.0"
     from ray.tune.analysis import ExperimentAnalysis as EA
 
     ray_import = True
diff --git a/flaml/version.py b/flaml/version.py
index 1f4c4d43b..5becc17c0 100644
--- a/flaml/version.py
+++ b/flaml/version.py
@@ -1 +1 @@
-__version__ = "0.10.1"
+__version__ = "1.0.0"
diff --git a/test/tune/test_searcher.py b/test/tune/test_searcher.py
index 61fcc3cb0..fd0dd7b3d 100644
--- a/test/tune/test_searcher.py
+++ b/test/tune/test_searcher.py
@@ -4,7 +4,7 @@ import numpy as np
 try:
     from ray import __version__ as ray_version
 
-    assert ray_version >= "1.0.0"
+    assert ray_version >= "1.10.0"
     from ray.tune import sample
 
     use_ray = True
diff --git a/website/docs/Use-Cases/Task-Oriented-AutoML.md b/website/docs/Use-Cases/Task-Oriented-AutoML.md
index b045a690f..1bc712389 100644
--- a/website/docs/Use-Cases/Task-Oriented-AutoML.md
+++ b/website/docs/Use-Cases/Task-Oriented-AutoML.md
@@ -11,10 +11,13 @@
     - 'classification': classification.
     - 'regression': regression.
     - 'ts_forecast': time series forecasting.
+    - 'ts_forecast_classification': time series forecasting for classification.
     - 'rank': learning to rank.
     - 'seq-classification': sequence classification.
     - 'seq-regression': sequence regression.
     - 'summarization': text summarization.
+    - 'token-classification': token classification.
+    - 'multichoice-classification': multichoice classification.
 
 An optional input is `time_budget` for searching models and hyperparameters. When not specified, a default budget of 60 seconds will be used.
 
@@ -104,18 +107,18 @@ The estimator list can contain one or more estimator names, each corresponding t
 
 #### Estimator
 * Built-in estimator.
-    - 'lgbm': LGBMEstimator. Hyperparameters: n_estimators, num_leaves, min_child_samples, learning_rate, log_max_bin (logarithm of (max_bin + 1) with base 2), colsample_bytree, reg_alpha, reg_lambda.
-    - 'xgboost': XGBoostSkLearnEstimator. Hyperparameters: n_estimators, max_leaves, max_depth, min_child_weight, learning_rate, subsample, colsample_bylevel, colsample_bytree, reg_alpha, reg_lambda.
-    - 'rf': RandomForestEstimator. Hyperparameters: n_estimators, max_features, max_leaves, criterion (for classification only).
-    - 'extra_tree': ExtraTreesEstimator. Hyperparameters: n_estimators, max_features, max_leaves, criterion (for classification only).
-    - 'lrl1': LRL1Classifier (sklearn.LogisticRegression with L1 regularization). Hyperparameters: C.
-    - 'lrl2': LRL2Classifier (sklearn.LogisticRegression with L2 regularization). Hyperparameters: C.
-    - 'catboost': CatBoostEstimator. Hyperparameters: early_stopping_rounds, learning_rate, n_estimators.
-    - 'kneighbor': KNeighborsEstimator. Hyperparameters: n_neighbors.
-    - 'prophet': Prophet. Hyperparameters: changepoint_prior_scale, seasonality_prior_scale, holidays_prior_scale, seasonality_mode.
-    - 'arima': ARIMA. Hyperparameters: p, d, q.
-    - 'sarimax': SARIMAX. Hyperparameters: p, d, q, P, D, Q, s.
-    - 'transformer': Huggingface transformer models. Hyperparameters: learning_rate, num_train_epochs, per_device_train_batch_size, warmup_ratio, weight_decay, adam_epsilon, seed.
+    - 'lgbm': LGBMEstimator for task "classification", "regression", "rank", "ts_forecast" and "ts_forecast_classification". Hyperparameters: n_estimators, num_leaves, min_child_samples, learning_rate, log_max_bin (logarithm of (max_bin + 1) with base 2), colsample_bytree, reg_alpha, reg_lambda.
+    - 'xgboost': XGBoostSkLearnEstimator for task "classification", "regression", "rank", "ts_forecast" and "ts_forecast_classification". Hyperparameters: n_estimators, max_leaves, max_depth, min_child_weight, learning_rate, subsample, colsample_bylevel, colsample_bytree, reg_alpha, reg_lambda.
+    - 'rf': RandomForestEstimator for task "classification", "regression", "ts_forecast" and "ts_forecast_classification". Hyperparameters: n_estimators, max_features, max_leaves, criterion (for classification only).
+    - 'extra_tree': ExtraTreesEstimator for task "classification", "regression", "ts_forecast" and "ts_forecast_classification". Hyperparameters: n_estimators, max_features, max_leaves, criterion (for classification only).
+    - 'lrl1': LRL1Classifier (sklearn.LogisticRegression with L1 regularization) for task "classification". Hyperparameters: C.
+    - 'lrl2': LRL2Classifier (sklearn.LogisticRegression with L2 regularization) for task "classification". Hyperparameters: C.
+    - 'catboost': CatBoostEstimator for task "classification" and "regression". Hyperparameters: early_stopping_rounds, learning_rate, n_estimators.
+    - 'kneighbor': KNeighborsEstimator for task "classification" and "regression". Hyperparameters: n_neighbors.
+    - 'prophet': Prophet for task "ts_forecast". Hyperparameters: changepoint_prior_scale, seasonality_prior_scale, holidays_prior_scale, seasonality_mode.
+    - 'arima': ARIMA for task "ts_forecast". Hyperparameters: p, d, q.
+    - 'sarimax': SARIMAX for task "ts_forecast". Hyperparameters: p, d, q, P, D, Q, s.
+    - 'transformer': Huggingface transformer models for task "seq-classification", "seq-regression", "multichoice-classification", "token-classification" and "summarization". Hyperparameters: learning_rate, num_train_epochs, per_device_train_batch_size, warmup_ratio, weight_decay, adam_epsilon, seed.
 * Custom estimator. Use custom estimator for:
     - tuning an estimator that is not built-in;
     - customizing search space for a built-in estimator.
diff --git a/website/yarn.lock b/website/yarn.lock
index b1f141ad1..9059dec6c 100644
--- a/website/yarn.lock
+++ b/website/yarn.lock
@@ -5485,7 +5485,7 @@ node-fetch@2.6.1:
   version "3.1.1"
 
 node-forge@^0.10.0:
-  version "1.0.0"
+  version "1.3.0"
 
 node-releases@^2.0.1:
   version "2.0.1"