[Docs] Reorganize pooling docs. (#35592)

Signed-off-by: wang.yuqi <yuqi.wang@daocloud.io>
Signed-off-by: wang.yuqi <noooop@126.com>
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>
Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

docs/models/pooling_models/token_classify.md

@@ -0,0 +1,89 @@
+# Token Classification Usages
+
+## Summary
+
+- Model Usage: token classification
+- Pooling Tasks: `token_classify`
+- Offline APIs:
+    - `LLM.encode(..., pooling_task="token_classify")`
+- Online APIs:
+    - Pooling API (`/pooling`)
+
+The key distinction between (sequence) classification and token classification lies in their output granularity: (sequence) classification produces a single result for an entire input sequence, whereas token classification yields a result for each individual token within the sequence.
+
+Many classification models support both (sequence) classification and token classification. For further details on (sequence) classification, please refer to [this page](classify.md).
+
+## Typical Use Cases
+
+### Named Entity Recognition (NER)
+
+For implementation examples, see:
+
+Offline: [examples/pooling/token_classify/ner_offline.py](../../../examples/pooling/token_classify/ner_offline.py)
+
+Online: [examples/pooling/token_classify/ner_online.py](../../../examples/pooling/token_classify/ner_online.py)
+
+### Sparse retrieval (lexical matching)
+
+The BAAI/bge-m3 model leverages token classification for sparse retrieval. For more information, see [this page](specific_models.md#baaibge-m3).
+
+## Supported Models
+
+| Architecture | Models | Example HF Models | [LoRA](../../features/lora.md) | [PP](../../serving/parallelism_scaling.md) |
+| ------------ | ------ | ----------------- | --------------------------- | --------------------------------------- |
+| `BertForTokenClassification` | bert-based | `boltuix/NeuroBERT-NER` (see note), etc. | | |
+| `ErnieForTokenClassification` | BERT-like Chinese ERNIE | `gyr66/Ernie-3.0-base-chinese-finetuned-ner` | | |
+| `ModernBertForTokenClassification` | ModernBERT-based | `disham993/electrical-ner-ModernBERT-base` | | |
+| `Qwen3ForTokenClassification`<sup>C</sup> | Qwen3-based | `bd2lcco/Qwen3-0.6B-finetuned` | | |
+| `*Model`<sup>C</sup>, `*ForCausalLM`<sup>C</sup>, etc. | Generative models | N/A | \* | \* |
+
+<sup>C</sup> Automatically converted into a classification model via `--convert classify`. ([details](./README.md#model-conversion))  
+\* Feature support is the same as that of the original model.
+
+If your model is not in the above list, we will try to automatically convert the model using
+[as_seq_cls_model][vllm.model_executor.models.adapters.as_seq_cls_model]. By default, the class probabilities are extracted from the softmaxed hidden state corresponding to the last token.
+
+### As Reward Models
+
+Using token classification models as reward models. For details on reward models, see [Reward Models](reward.md).
+
+--8<-- "docs/models/pooling_models/reward.md:supported-token-reward-models"
+
+## Offline Inference
+
+### Pooling Parameters
+
+The following [pooling parameters][vllm.PoolingParams] are supported.
+
+```python
+--8<-- "vllm/pooling_params.py:common-pooling-params"
+--8<-- "vllm/pooling_params.py:classify-pooling-params"
+```
+
+### `LLM.encode`
+
+The [encode][vllm.LLM.encode] method is available to all pooling models in vLLM.
+
+Set `pooling_task="token_classify"` when using `LLM.encode` for token classification Models:
+
+```python
+from vllm import LLM
+
+llm = LLM(model="boltuix/NeuroBERT-NER", runner="pooling")
+(output,) = llm.encode("Hello, my name is", pooling_task="token_classify")
+
+data = output.outputs.data
+print(f"Data: {data!r}")
+```
+
+## Online Serving
+
+Please refer to the [pooling API](README.md#pooling-api) and use `"task":"token_classify"`.
+
+## More examples
+
+More examples can be found here: [examples/pooling/token_classify](../../../examples/pooling/token_classify)
+
+## Supported Features
+
+Token classification features should be consistent with (sequence) classification. For more information, see [this page](classify.md#supported-features).