[New Model] Support BertForTokenClassification / Named Entity Recognition (NER) task (#24872)

Signed-off-by: wang.yuqi <noooop@126.com>
Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>
Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn>

docs/models/supported_models.md

@@ -554,6 +554,17 @@ If your model is not in the above list, we will try to automatically convert the
     For process-supervised reward models such as `peiyi9979/math-shepherd-mistral-7b-prm`, the pooling config should be set explicitly,
     e.g.: `--override-pooler-config '{"pooling_type": "STEP", "step_tag_id": 123, "returned_token_ids": [456, 789]}'`.
 
+#### Token Classification
+
+These models primarily support the [`LLM.encode`](./pooling_models.md#llmencode) API.
+
+| Architecture | Models | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/parallelism_scaling.md) | [V1](gh-issue:8779) |
+|--------------|--------|-------------------|-----------------------------|-----------------------------------------|---------------------|
+| `BertForTokenClassification` | bert-based | `boltuix/NeuroBERT-NER` (see note), etc. |  |  | ✅︎ |
+
+!!! note
+    Named Entity Recognition (NER) usage, please refer to <gh-file:examples/offline_inference/pooling/ner.py>, <gh-file:examples/online_serving/pooling/ner.py>.
+
 [](){ #supported-mm-models }
 
 ## List of Multimodal Language Models