add model markdown doc

docs/source/en/model_doc/plm.md

@@ -0,0 +1,54 @@
+<!--Copyright 2024 The PLM Team and The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# PLM
+
+## Overview
+
+To be released with the official model launch.
+
+### Model Details
+
+To be released with the official model launch.
+
+
+## Usage tips
+
+To be released with the official model launch.
+
+## PLMConfig
+
+[[autodoc]] PLMConfig
+
+## PLMModel
+
+[[autodoc]] PLMModel
+    - forward
+
+## PLMForCausalLM
+
+[[autodoc]] PLMForCausalLM
+    - forward
+
+## PLMForSequenceClassification
+
+[[autodoc]] PLMForSequenceClassification
+    - forward
+
+## PLMForTokenClassification
+
+[[autodoc]] PLMForTokenClassification
+    - forward

tests/models/plm/test_modeling_plm.py

@@ -40,6 +40,8 @@ if is_torch_available():
     from transformers import (
         PLMForCausalLM,
         PLMModel,
+        PLMForSequenceClassification,
+        PLMForTokenClassification,
     )
 
     # from transformers.models.plm.modeling_plm import (
@@ -319,6 +321,8 @@ class PLMModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin,
         (
             PLMModel,
             PLMForCausalLM,
+            PLMForSequenceClassification,
+            PLMForTokenClassification,
         )
         if is_torch_available()
         else ()
@@ -327,7 +331,10 @@ class PLMModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin,
     pipeline_model_mapping = (
         {
             "feature-extraction": PLMModel,
+            "text-classification": PLMForSequenceClassification,
+            "token-classification": PLMForTokenClassification,
             "text-generation": PLMForCausalLM,
+            "zero-shot": PLMForSequenceClassification,
         }
         if is_torch_available()
         else {}
@@ -423,6 +430,33 @@ class PLMModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin,
     def test_config(self):
         self.config_tester.run_common_tests()
 
+    def test_PLM_token_classification_model(self):
+        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.num_labels = 3
+        input_ids = input_dict["input_ids"]
+        attention_mask = input_ids.ne(1).to(torch_device)
+        token_labels = ids_tensor([self.model_tester.batch_size, self.model_tester.seq_length], config.num_labels)
+        model = PLMForTokenClassification(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=attention_mask, labels=token_labels)
+        self.assertEqual(
+            result.logits.shape,
+            (self.model_tester.batch_size, self.model_tester.seq_length, self.model_tester.num_labels),
+        )
+
+    def test_Qwen2_sequence_classification_model(self):
+        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.num_labels = 3
+        input_ids = input_dict["input_ids"]
+        attention_mask = input_ids.ne(1).to(torch_device)
+        sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
+        model = PLMForSequenceClassification(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
+        self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
+
     #  def test_model(self):
     #     config_and_inputs = self.model_tester.prepare_config_and_inputs()
     #     self.model_tester.create_and_check_model(*config_and_inputs)