[Model] Remove obsolete ERNIE models

docs/models/pooling_models/classify.md

@@ -31,7 +31,6 @@ The most fundamental application of classification models is to categorize input
 
 | Architecture | Models | Example HF Models | [LoRA](../../features/lora.md) | [PP](../../serving/parallelism_scaling.md) |
 | ------------ | ------ | ----------------- | ------------------------------ | ------------------------------------------ |
-| `ErnieForSequenceClassification` | BERT-like Chinese ERNIE | `Forrest20231206/ernie-3.0-base-zh-cls` | | |
 | `GPT2ForSequenceClassification` | GPT2 | `nie3e/sentiment-polish-gpt2-small` | | |
 | `Qwen2ForSequenceClassification`<sup>C</sup> | Qwen2-based | `jason9693/Qwen2.5-1.5B-apeach` | | |
 | `*Model`<sup>C</sup>, `*ForCausalLM`<sup>C</sup>, etc. | Generative models | N/A | \* | \* |

docs/models/pooling_models/embed.md

@@ -39,7 +39,6 @@ You can compute pairwise similarity scores to build a similarity matrix using th
 | ------------ | ------ | ----------------- | ------------------------------ | ------------------------------------------ |
 | `BertModel` | BERT-based | `BAAI/bge-base-en-v1.5`, `Snowflake/snowflake-arctic-embed-xs`, etc. | | |
 | `BertSpladeSparseEmbeddingModel` | SPLADE | `naver/splade-v3` | | |
-| `ErnieModel` | BERT-like Chinese ERNIE | `shibing624/text2vec-base-chinese-sentence` | | |
 | `Gemma2Model`<sup>C</sup> | Gemma 2-based | `BAAI/bge-multilingual-gemma2`, etc. | ✅︎ | ✅︎ |
 | `Gemma3TextModel`<sup>C</sup> | Gemma 3-based | `google/embeddinggemma-300m`, etc. | ✅︎ | ✅︎ |
 | `GritLM` | GritLM | `parasail-ai/GritLM-7B-vllm`. | ✅︎ | ✅︎ |

docs/models/pooling_models/token_classify.md

@@ -44,7 +44,6 @@ The BAAI/bge-m3 model leverages token classification for sparse retrieval. For m
 | 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 | \* | \* |

tests/models/language/pooling/test_classification.py

@@ -18,7 +18,6 @@
                 pytest.mark.slow_test,
             ],
         ),
-        pytest.param("Forrest20231206/ernie-3.0-base-zh-cls"),
     ],
 )
 @pytest.mark.parametrize("dtype", ["half"] if current_platform.is_rocm() else ["float"])
@@ -48,6 +47,5 @@ def test_models(
         assert torch.allclose(
             hf_output,
             vllm_output,
-            atol=1e-3 if dtype == "float" else 1e-2,
             rtol=2e-3 if dtype == "float" else 1e-2,
         )

tests/models/language/pooling/test_token_classification.py

@@ -24,13 +24,12 @@ def seed_everything():
     "model",
     [
         "boltuix/NeuroBERT-NER",
-        "gyr66/Ernie-3.0-base-chinese-finetuned-ner",
     ],
 )
 # The float32 is required for this tiny model to pass the test.
 @pytest.mark.parametrize("dtype", ["float"])
 @torch.inference_mode
-def test_bert_like_models(
+def test_bert_models(
     hf_runner,
     vllm_runner,
     example_prompts,

tests/models/registry.py

@@ -610,7 +610,6 @@ def check_available_online(
 _EMBEDDING_EXAMPLE_MODELS = {
     # [Text-only]
     "BertModel": _HfExamplesInfo("BAAI/bge-base-en-v1.5"),
-    "ErnieModel": _HfExamplesInfo("shibing624/text2vec-base-chinese-sentence"),
     "BertSpladeSparseEmbeddingModel": _HfExamplesInfo(
         "naver/splade-v3",
         hf_overrides={"architectures": ["BertSpladeSparseEmbeddingModel"]},
@@ -740,9 +739,6 @@ def check_available_online(
 
 _TOKEN_CLASSIFICATION_EXAMPLE_MODELS = {
     "BertForTokenClassification": _HfExamplesInfo("boltuix/NeuroBERT-NER"),
-    "ErnieForTokenClassification": _HfExamplesInfo(
-        "gyr66/Ernie-3.0-base-chinese-finetuned-ner"
-    ),
     "ModernBertForTokenClassification": _HfExamplesInfo(
         "disham993/electrical-ner-ModernBERT-base"
     ),
@@ -752,9 +748,6 @@ def check_available_online(
     "BertForSequenceClassification": _HfExamplesInfo(
         "cross-encoder/ms-marco-MiniLM-L-6-v2"
     ),
-    "ErnieForSequenceClassification": _HfExamplesInfo(
-        "Forrest20231206/ernie-3.0-base-zh-cls",
-    ),
     "GPT2ForSequenceClassification": _HfExamplesInfo(
         "nie3e/sentiment-polish-gpt2-small"
     ),