Initial release: FormBench TAPT-MNRL model (anonymised for review)

1_Pooling/config.json

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+{
+    "word_embedding_dimension": 1024,
+    "pooling_mode_cls_token": true,
+    "pooling_mode_mean_tokens": false,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}

README.md

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+---
+license: apache-2.0
+base_model: Alibaba-NLP/gte-large-en-v1.5
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- formbench
+- patent-retrieval
+- chemistry
+- formulations
+- materials-science
+language:
+- en
+pipeline_tag: sentence-similarity
+---
+
+# gte-large-formbench-mnrl
+
+A domain-adapted sentence-transformers model derived from
+[`Alibaba-NLP/gte-large-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) and fine-tuned on the **FormBench**
+retrieval benchmark for formulation chemistry. It maps passages from formulation patents
+into a 1024-dimensional dense vector space and is optimised for within-domain
+retrieval among structurally similar near-miss passages — the central capability targeted
+by FormBench.
+
+This repository hosts an anonymised release for NeurIPS 2026 double-blind review.
+
+## Model details
+
+| Item | Value |
+|---|---|
+| Base model | `Alibaba-NLP/gte-large-en-v1.5` (434M params) |
+| Training method | Task-adaptive pre-training (TAPT) via contrastive fine-tuning |
+| Loss | `MultipleNegativesRankingLoss` (in-batch negatives) |
+| Training data | FormBench-Triplets — 44,413 (query, anchor, hard-negative) tuples |
+| Embedding dimension | 1024 |
+| Max sequence length | 8192 (training: 2048) |
+| Precision | bf16 |
+| Learning rate | 1e-5 |
+| Per-GPU batch size | 64 |
+| Epochs | 5 |
+| Hardware | 8× AMD MI250X, DDP |
+
+The training-triplet set is reconstructable from the qrel files in
+[`Formbench-anon/FormBench`](https://huggingface.co/datasets/Formbench-anon/FormBench)
+following the protocol in §3 of the paper.
+
+## Evaluation results
+
+Evaluated on the FormBench test split (n = 5,459 queries) under both corpus variants,
+following the protocol in §4 of the paper. FAISS exact inner-product search at top-k = 100.
+
+### FormBench-Structured (C1) — within-domain near-miss distractors
+
+| Metric | Value |
+|---|---:|
+| Binary nDCG@10 | **0.3561** |
+| MRR (binary qrels) | 0.3125 |
+| Graded nDCG@10 | 0.2087 |
+| R@100 (binary qrels) | 0.7586 |
+| FAISS search latency | 18.9 ms/query |
+
+### FormBench-Random (C0) — random-distractor corpus
+
+| Metric | Value |
+|---|---:|
+| Binary nDCG@10 | **0.4308** |
+| MRR (binary qrels) | 0.3883 |
+| Graded nDCG@10 | 0.2553 |
+| R@100 (binary qrels) | 0.8011 |
+| FAISS search latency | 18.8 ms/query |
+
+For reference: BM25 lexical baseline: binary nDCG@10 = 0.3751 (C1), 0.4665 (C0).
+
+## Usage
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("Formbench-anon/gte-large-formbench-mnrl")
+
+passages = [
+    "An adhesive composition comprising a styrene-acrylate copolymer ...",
+    "A water-based latex paint formulation containing ...",
+]
+queries = [
+    "what wax-seeded latex polymers improve scuff resistance in architectural coatings?",
+]
+
+passage_embeds = model.encode(passages, normalize_embeddings=True)
+query_embeds   = model.encode(queries,  normalize_embeddings=True)
+```
+
+## Intended use
+
+Domain-specific retrieval over formulation patents — adhesives, coatings, lubricants,
+pharmaceuticals, agrochemicals, personal care, food. Particularly suited to
+within-domain near-miss discrimination, where general-purpose embedders have been shown
+to fail.
+
+## Limitations
+
+- Training queries are LLM-generated (Sonnet 4 + Haiku 4.5 quality filter) and may not
+  match real practitioner intent.
+- Coverage limited to USPTO utility patents (1995–2022) in English only.
+- Performance on out-of-domain retrieval is not characterised.
+
+## Citation
+
+```bibtex
+@misc{formbench2026,
+  title  = { {FormBench}: Evaluating Chemical Knowledge Retrieval in Formulation Patents },
+  author = { Anonymous Authors },
+  year   = { 2026 },
+  note   = { Under double-blind review at NeurIPS 2026 Datasets \& Benchmarks Track }
+}
+```
+
+## License
+
+Apache 2.0, inherited from the base model.

_eval_status.json

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+{
+  "c1": {
+    "status": "completed",
+    "queued_by_job": "4376439",
+    "started_at": "2026-04-11T00:16:02.927797",
+    "phase1_job": "4376439",
+    "phase1_completed_at": "2026-04-11T00:26:42.439787",
+    "completed_at": "2026-04-11T02:47:29.358781",
+    "metrics_path": "/lustre/orion/mat721/proj-shared/formbench_ner/experiments/results/gte_large_tapt_mnrl_ckpt-85/c1/metrics.json"
+  },
+  "c0": {
+    "status": "completed",
+    "queued_by_job": "4379497",
+    "started_at": "2026-04-12T08:54:12.288295",
+    "phase1_job": "4379497",
+    "phase1_completed_at": "2026-04-12T09:03:23.968890",
+    "completed_at": "2026-04-12T11:45:23.541570",
+    "metrics_path": "/lustre/orion/mat721/proj-shared/formbench_ner/experiments/results/gte_large_tapt_mnrl_ckpt-85/c0/metrics.json"
+  }
+}

_legacy_eval_status.json

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+{
+  "status": "completed",
+  "corpus": "c1",
+  "queued_by_job": "4376439",
+  "started_at": "2026-04-11T00:16:02.927797",
+  "phase1_job": "4376439",
+  "phase1_completed_at": "2026-04-11T00:26:42.439787",
+  "completed_at": "2026-04-11T02:47:29.358781",
+  "metrics_path": "/lustre/orion/mat721/proj-shared/formbench_ner/experiments/results/gte_large_tapt_mnrl_ckpt-85/c1/metrics.json"
+}

config.json

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+{
+  "architectures": [
+    "NewModel"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration.NewConfig",
+    "AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel",
+    "AutoModelForMaskedLM": "Alibaba-NLP/new-impl--modeling.NewForMaskedLM",
+    "AutoModelForMultipleChoice": "Alibaba-NLP/new-impl--modeling.NewForMultipleChoice",
+    "AutoModelForQuestionAnswering": "Alibaba-NLP/new-impl--modeling.NewForQuestionAnswering",
+    "AutoModelForSequenceClassification": "Alibaba-NLP/new-impl--modeling.NewForSequenceClassification",
+    "AutoModelForTokenClassification": "Alibaba-NLP/new-impl--modeling.NewForTokenClassification"
+  },
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "layer_norm_type": "layer_norm",
+  "logn_attention_clip1": false,
+  "logn_attention_scale": false,
+  "max_position_embeddings": 8192,
+  "model_type": "new",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pack_qkv": true,
+  "pad_token_id": 0,
+  "position_embedding_type": "rope",
+  "rope_scaling": {
+    "factor": 2.0,
+    "type": "ntk"
+  },
+  "rope_theta": 160000,
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 2,
+  "unpad_inputs": false,
+  "use_memory_efficient_attention": false,
+  "vocab_size": 30528
+}

config_sentence_transformers.json

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+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.3.0",
+    "transformers": "4.51.3",
+    "pytorch": "2.3.1+rocm5.7"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

configuration.py

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+# coding=utf-8
+# Copyright 2024 The GTE Team Authors and Alibaba Group.
+# Copyright (c) 2018, NVIDIA CORPORATION.  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.
+""" NEW model configuration"""
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class NewConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`NewModel`] or a [`TFNewModel`]. It is used to
+    instantiate a NEW model according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the NEW
+    [izhx/new-base-en](https://huggingface.co/izhx/new-base-en) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 30522):
+            Vocabulary size of the NEW model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`NewModel`] or [`TFNewModel`].
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+        hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"silu"` and `"gelu_new"` are supported.
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout ratio for the attention probabilities.
+        max_position_embeddings (`int`, *optional*, defaults to 512):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        type_vocab_size (`int`, *optional*, defaults to 2):
+            The vocabulary size of the `token_type_ids` passed when calling [`NewModel`] or [`TFNewModel`].
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        position_embedding_type (`str`, *optional*, defaults to `"rope"`):
+            Type of position embedding. Choose one of `"absolute"`, `"rope"`.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+        classifier_dropout (`float`, *optional*):
+            The dropout ratio for the classification head.
+
+    Examples:
+
+    ```python
+    >>> from transformers import NewConfig, NewModel
+
+    >>> # Initializing a NEW izhx/new-base-en style configuration
+    >>> configuration = NewConfig()
+
+    >>> # Initializing a model (with random weights) from the izhx/new-base-en style configuration
+    >>> model = NewModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "new"
+
+    def __init__(
+        self,
+        vocab_size=30528,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        intermediate_size=3072,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.0,
+        max_position_embeddings=2048,
+        type_vocab_size=1,
+        initializer_range=0.02,
+        layer_norm_type='layer_norm',
+        layer_norm_eps=1e-12,
+        # pad_token_id=0,
+        position_embedding_type="rope",
+        rope_theta=10000.0,
+        rope_scaling=None,
+        classifier_dropout=None,
+        pack_qkv=True,
+        unpad_inputs=False,
+        use_memory_efficient_attention=False,
+        logn_attention_scale=False,
+        logn_attention_clip1=False,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.initializer_range = initializer_range
+        self.layer_norm_type = layer_norm_type
+        self.layer_norm_eps = layer_norm_eps
+        self.position_embedding_type = position_embedding_type
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.classifier_dropout = classifier_dropout
+
+        self.pack_qkv = pack_qkv
+        self.unpad_inputs = unpad_inputs
+        self.use_memory_efficient_attention = use_memory_efficient_attention
+        self.logn_attention_scale = logn_attention_scale
+        self.logn_attention_clip1 = logn_attention_clip1

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

optimizer.pt

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