Resave model without delay tie reload requirement

Resaved with current asmtransformers so ASMSentenceTransformer loads without the delay_tie_for_load compatibility path. Verified embeddings match the resaved branch output.

1_Pooling/config.json

@@ -1,9 +1,5 @@
-{
-  "word_embedding_dimension": 768,
-  "pooling_mode_cls_token": false,
-  "pooling_mode_mean_tokens": true,
-  "pooling_mode_max_tokens": false,
-  "pooling_mode_mean_sqrt_len_tokens": false,
-  "pooling_mode_weightedmean_tokens": false,
-  "pooling_mode_lasttoken": false
+{
+    "embedding_dimension": 768,
+    "pooling_mode": "mean",
+    "include_prompt": true
 }

README.md

@@ -1,79 +1,140 @@
 ---
-license: eupl-1.2
-language: code
-base_model:
-- NetherlandsForensicInstitute/ARM64BERT
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+base_model: NetherlandsForensicInstitute/ARM64BERT-embedding
+pipeline_tag: sentence-similarity
 library_name: sentence-transformers
 ---
 
-ARM64BERT-embedding 🦾
-======================
-
-[GitHub repository](https://github.com/NetherlandsForensicInstitute/asmtransformers)
-
-## General
-### What is the purpose of the model
-The model is a BERT model of ARM64 assembly code that can be used to find similar ARM64 functions to a given ARM64 function.
-This task is known as _binary code similarity detection_, which is similar to the _sentence similarity_ task in natural language processing.
-
-### What does the model architecture look like?	
-The model architecture is inspired by [jTrans](https://github.com/vul337/jTrans) (Wang et al., 2022). 
-It is a BERT model (Devlin et al. 2019) although the typical Next Sentence Prediction has been replaced with Jump Target Prediction, as proposed in Wang et al. 
-This architecture has subsequently been finetuned for semantic search purposes. We have followed the procedure proposed by [S-BERT](https://www.sbert.net/examples/applications/semantic-search/README.html).
-
-### What is the output of the model?
-The model returns an embedding vector of 768 dimensions for each function that it's given. These embeddings can be compared to
-get an indication of which functions are similar to each other.
-
-### How does the model perform?
-The model has been evaluated on [Mean Reciprocal Rank (MRR)](https://en.wikipedia.org/wiki/Mean_reciprocal_rank) and 
-[Recall@1](https://en.wikipedia.org/wiki/Precision_and_recall).
-When the model has to pick the positive example out of a pool of 32, ranks the positive example highest most of the time. 
-When the pool is significantly enlarged to 10.000 functions, it still ranks the positive example first or second in most cases.
-
-| Model                | Pool size | MRR  | Recall@1 |
-|----------------------|-----------|------|----------|
-| ARM64BERT            | 32        | 0.78 | 0.72     |
-| ARM64BERT-embedding  | 32        | 0.99 | 0.99     |
-| ARM64BERT            | 10.000    | 0.58 | 0.56     |
-| ARM64BERT-embedding  | 10.000    | 0.87 | 0.83     |
-
-## Purpose and use of the model
-
-### For which problem has the model been designed?
-The model has been designed to find similar ARM64 functions in a database of known ARM64 functions.
-
-### What else could the model be used for?
-We do not see other applications for this model. 
-
-### To what problems is the model not applicable?
-This model has been finetuned on the semantic search task.
-For the base ARM64BERT model, please refer to the [other 
-model](https://huggingface.co/NetherlandsForensicInstitute/ARM64BERT) we have published. 	
-
-## Data
-### What data was used for training and evaluation?
-The dataset is created in the same way as Wang et al. created Binary Corp. 
-A large set of source code comes from the [ArchLinux official repositories](https://archlinux.org/packages/) and the [ArchLinux user repositories](https://aur.archlinux.org/packages/).
-All this code is split into functions that are compiled into binary code with different optimalizations 
-(`O0`, `O1`, `O2`, `O3` and `Os`) and security settings (fortify or no-fortify).
-This results in a maximum of 10 (5×2) different functions which are semantically similar, i.e. they represent the same functionality, but have different machine code. 
-The dataset is split into a train and a test set. This is done on project level, so all binaries and functions belonging to one project are part of 
-either the train or the test set, not both. We have not performed any deduplication on the dataset for training.  
-
-| set   | # functions |
-|-------|------------:|
-| train |  18,083,285 |
-| test  |   3,375,741 |
-
-For our training and evaluation code, see our [GitHub repository](https://github.com/NetherlandsForensicInstitute/asmtransformers).
-
-
-### By whom was the dataset collected and annotated?
-The dataset was collected by our team. 
-
-### Any remarks on data quality and bias?	
-After training our models, we found out that something had gone wrong when compiling our dataset.
-Consequently, the first line of the next function was included in the previous.
-This has been fixed for the finetuning, but due to the long training process, 
-and the good performance of the model despite the mistake, we have decided not to retrain the base model. 
+# ASMSentenceTransformer based on NetherlandsForensicInstitute/ARM64BERT-embedding
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [NetherlandsForensicInstitute/ARM64BERT-embedding](https://huggingface.co/NetherlandsForensicInstitute/ARM64BERT-embedding). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [NetherlandsForensicInstitute/ARM64BERT-embedding](https://huggingface.co/NetherlandsForensicInstitute/ARM64BERT-embedding) <!-- at revision d6ccf7f11bb9a1c5d2e690db4e26837edf9d4a10 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Supported Modality:** Text
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+ASMSentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'architecture': 'ASMBertModel'})
+  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'The weather is lovely today.',
+    "It's so sunny outside!",
+    'He drove to the stadium.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Framework Versions
+- Python: 3.13.13
+- Sentence Transformers: 5.4.1
+- Transformers: 5.6.0
+- PyTorch: 2.11.0
+- Accelerate: 1.13.0
+- Datasets: 4.8.4
+- Tokenizers: 0.22.2
+
+## Citation
+
+### BibTeX
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -1,25 +1,30 @@
-{
-  "_name_or_path": "D:\\asmtransformers\\output\\models\\pretraining_mlm_2023-12-21_16-08-17",
-  "architectures": [
-    "ASMBertModel"
-  ],
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-  "model_type": "bert",
-  "num_attention_heads": 12,
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-  "pad_token_id": 0,
-  "position_embedding_type": "absolute",
-  "torch_dtype": "float32",
-  "transformers_version": "4.34.1",
-  "type_vocab_size": 2,
-  "use_cache": true,
-  "vocab_size": 2756
-}
+{
+  "add_cross_attention": false,
+  "architectures": [
+    "ASMBertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": null,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
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+  "initializer_range": 0.02,
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+  "is_decoder": false,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
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+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "tie_word_embeddings": true,
+  "tokenizer_class": "ARM64Tokenizer",
+  "transformers_version": "5.6.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 2756
+}

config_sentence_transformers.json

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-{
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-    "sentence_transformers": "2.3.1",
-    "transformers": "4.34.1",
-    "pytorch": "2.1.1+cu118"
-  }
+{
+  "__version__": {
+    "pytorch": "2.11.0",
+    "sentence_transformers": "5.4.1",
+    "transformers": "5.6.0"
+  },
+  "default_prompt_name": null,
+  "model_type": "ASMSentenceTransformer",
+  "prompts": {
+    "document": "",
+    "query": ""
+  },
+  "similarity_fn_name": "cosine"
 }

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-size 351081176
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modules.json

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-[
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-  }
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+  {
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+    "name": "0",
+    "path": "",
+    "type": "asmtransformers.models.asmsentencebert.ASMSTTransformer"
+  },
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+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
+  }
 ]

sentence_bert_config.json

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-{
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-  "do_lower_case": false
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+    "modality_config": {
+        "text": {
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+            "method_output_name": "last_hidden_state"
+        }
+    }
 }

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+      "b18": 776,
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+      "b20": 779,
+      "b21": 780,
+      "b22": 781,
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+      "b24": 783,
+      "b25": 784,
+      "b26": 785,
+      "b27": 786,
+      "b28": 787,
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+      "b3": 789,
+      "b30": 790,
+      "b31": 791,
+      "b4": 792,
+      "b5": 793,
+      "b6": 794,
+      "b7": 795,
+      "b8": 796,
+      "b9": 797,
+      "bcax": 798,
+      "bfm": 799,
+      "bfxil": 800,
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+      "blr": 806,
+      "br": 807,
+      "brk": 808,
+      "bsl": 809,
+      "bti": 810,
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+      "c0x0": 812,
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+      "c0x4": 814,
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+      "casa": 817,
+      "casab": 818,
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+      "casalh": 821,
+      "casb": 822,
+      "casl": 823,
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+      "cbz": 827,
+      "cc": 828,
+      "ccmn": 829,
+      "ccmp": 830,
+      "cinc": 831,
+      "cinv": 832,
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+      "cls": 834,
+      "clz": 835,
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+      "cmhi": 839,
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+      "cmlt": 842,
+      "cmn": 843,
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+      "cmtst": 845,
+      "cneg": 846,
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+      "crc32cb": 850,
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+      "crc32cw": 852,
+      "crc32cx": 853,
+      "crc32h": 854,
+      "crc32w": 855,
+      "crc32x": 856,
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+      "d11": 869,
+      "d12": 870,
+      "d13": 871,
+      "d14": 872,
+      "d15": 873,
+      "d16": 874,
+      "d17": 875,
+      "d18": 876,
+      "d19": 877,
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+      "d20": 879,
+      "d21": 880,
+      "d22": 881,
+      "d23": 882,
+      "d24": 883,
+      "d25": 884,
+      "d26": 885,
+      "d27": 886,
+      "d28": 887,
+      "d29": 888,
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+      "d30": 890,
+      "d31": 891,
+      "d4": 892,
+      "d5": 893,
+      "d6": 894,
+      "d7": 895,
+      "d8": 896,
+      "d9": 897,
+      "dc": 898,
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+      "extr": 907,
+      "fabd": 908,
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+      "fmaxnmp": 942,
+      "fmaxnmv": 943,
+      "fmaxv": 944,
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+      "fminnm": 946,
+      "fminnmp": 947,
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+      "fmla": 949,
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+      "fmsub": 952,
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+      "fnmls": 956,
+      "fnmsub": 957,
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+      "frecps": 962,
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+      "frinti": 964,
+      "frintm": 965,
+      "frintn": 966,
+      "frintp": 967,
+      "frintx": 968,
+      "frintz": 969,
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+      "h0": 976,
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+      "h10": 978,
+      "h11": 979,
+      "h12": 980,
+      "h13": 981,
+      "h14": 982,
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+      "h16": 984,
+      "h17": 985,
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+      "h19": 987,
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+      "h21": 990,
+      "h22": 991,
+      "h23": 992,
+      "h24": 993,
+      "h25": 994,
+      "h26": 995,
+      "h27": 996,
+      "h28": 997,
+      "h29": 998,
+      "h3": 999,
+      "h30": 1000,
+      "h31": 1001,
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+      "h5": 1003,
+      "h6": 1004,
+      "h7": 1005,
+      "h8": 1006,
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+      "ic": 1009,
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+      "id_aa64isar1_el1": 1011,
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+      "ldaddalb": 1030,
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+      "ldclrl": 1046,
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+      "ldsetal": 1061,
+      "ldsetalb": 1062,
+      "ldsetalh": 1063,
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+      "ldsetl": 1065,
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+      "ldurh": 1068,
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+      "ldursh": 1070,
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+      "ldxrh": 1075,
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+      "lsl": 1078,
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+      "mla": 1085,
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+      "mneg": 1087,
+      "mov": 1088,
+      "movi": 1089,
+      "movk": 1090,
+      "mrs": 1091,
+      "msl": 1092,
+      "msr": 1093,
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+      "mvn": 1096,
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+      "ne": 1098,
+      "neg": 1099,
+      "negs": 1100,
+      "ngc": 1101,
+      "ngcs": 1102,
+      "nop": 1103,
+      "nzcv": 1104,
+      "orn": 1105,
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+      "p0.b": 1107,
+      "p10/z": 1108,
+      "p3/m": 1109,
+      "p4/m": 1110,
+      "p5.b": 1111,
+      "p9.b": 1112,
+      "paciasp": 1113,
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+      "q10": 1126,
+      "q11": 1127,
+      "q12": 1128,
+      "q13": 1129,
+      "q14": 1130,
+      "q15": 1131,
+      "q16": 1132,
+      "q17": 1133,
+      "q18": 1134,
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+      "q20": 1137,
+      "q21": 1138,
+      "q22": 1139,
+      "q23": 1140,
+      "q24": 1141,
+      "q25": 1142,
+      "q26": 1143,
+      "q27": 1144,
+      "q28": 1145,
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+      "q30": 1148,
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+      "q4": 1150,
+      "q5": 1151,
+      "q6": 1152,
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+      "q9": 1155,
+      "raddhn": 1156,
+      "raddhn2": 1157,
+      "rax1": 1158,
+      "rbit": 1159,
+      "ret": 1160,
+      "rev": 1161,
+      "rev16": 1162,
+      "rev32": 1163,
+      "rev64": 1164,
+      "ror": 1165,
+      "rshrn": 1166,
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+      "s10": 1171,
+      "s11": 1172,
+      "s12": 1173,
+      "s13": 1174,
+      "s14": 1175,
+      "s15": 1176,
+      "s16": 1177,
+      "s17": 1178,
+      "s18": 1179,
+      "s19": 1180,
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+      "s20": 1182,
+      "s21": 1183,
+      "s22": 1184,
+      "s23": 1185,
+      "s24": 1186,
+      "s25": 1187,
+      "s26": 1188,
+      "s27": 1189,
+      "s28": 1190,
+      "s29": 1191,
+      "s3": 1192,
+      "s30": 1193,
+      "s31": 1194,
+      "s4": 1195,
+      "s5": 1196,
+      "s6": 1197,
+      "s7": 1198,
+      "s8": 1199,
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+      "sha1m": 1221,
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+      "sha256su0": 1227,
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+      "shadd": 1233,
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+      "sqrdmulh": 1260,
+      "sqrshrn": 1261,
+      "sqrshrn2": 1262,
+      "sqrshrun": 1263,
+      "sqrshrun2": 1264,
+      "sqshrun": 1265,
+      "sqsub": 1266,
+      "sqxtn": 1267,
+      "sqxtn2": 1268,
+      "sqxtun": 1269,
+      "sqxtun2": 1270,
+      "sreg(0x0": 1271,
+      "sreg(0x3": 1272,
+      "srhadd": 1273,
+      "sri": 1274,
+      "srshl": 1275,
+      "srshr": 1276,
+      "sshl": 1277,
+      "sshll": 1278,
+      "sshll2": 1279,
+      "sshr": 1280,
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+      "ssubw": 1284,
+      "ssubw2": 1285,
+      "st": 1286,
+      "st1": 1287,
+      "st2": 1288,
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+      "stlxp": 1294,
+      "stlxr": 1295,
+      "stlxrb": 1296,
+      "stlxrh": 1297,
+      "stnp": 1298,
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+      "str": 1300,
+      "strb": 1301,
+      "strh": 1302,
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+      "stxrb": 1308,
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+      "swpa": 1314,
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+      "swpal": 1316,
+      "swpalb": 1317,
+      "swpalh": 1318,
+      "swpb": 1319,
+      "swpl": 1320,
+      "swplb": 1321,
+      "sxtb": 1322,
+      "sxth": 1323,
+      "sxtl": 1324,
+      "sxtl2": 1325,
+      "sxtw": 1326,
+      "tbl": 1327,
+      "tbnz": 1328,
+      "tbz": 1329,
+      "tpidr_el0": 1330,
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+      "trn2": 1332,
+      "tst": 1333,
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+      "uabal2": 1335,
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+      "uaddlp": 1342,
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+      "ucvtf": 1348,
+      "udf": 1349,
+      "udiv": 1350,
+      "udot": 1351,
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+      "umaxp": 1355,
+      "umaxv": 1356,
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+      "uminv": 1359,
+      "umlal": 1360,
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+      "umlsl": 1362,
+      "umnegl": 1363,
+      "umov": 1364,
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+      "uqshl": 1372,
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+      "uqshrn2": 1374,
+      "uqsub": 1375,
+      "uqxtn": 1376,
+      "urhadd": 1377,
+      "urshr": 1378,
+      "ushl": 1379,
+      "ushll": 1380,
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+      "ushr": 1382,
+      "usra": 1383,
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+      "usubw2": 1387,
+      "uxtb": 1388,
+      "uxth": 1389,
+      "uxtl": 1390,
+      "uxtl2": 1391,
+      "uxtw": 1392,
+      "uxtx": 1393,
+      "uzp1": 1394,
+      "uzp2": 1395,
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+      "v0.1d": 1397,
+      "v0.1q": 1398,
+      "v0.2d": 1399,
+      "v0.2s": 1400,
+      "v0.4h": 1401,
+      "v0.4s": 1402,
+      "v0.8b": 1403,
+      "v0.8h": 1404,
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+      "v0.b[0x0]": 1406,
+      "v0.b[0x1]": 1407,
+      "v0.b[0x2]": 1408,
+      "v0.b[0x3]": 1409,
+      "v0.b[0x4]": 1410,
+      "v0.b[0x5]": 1411,
+      "v0.b[0x6]": 1412,
+      "v0.b[0x7]": 1413,
+      "v0.b[0x8]": 1414,
+      "v0.b[0x9]": 1415,
+      "v0.b[0xa]": 1416,
+      "v0.b[0xb]": 1417,
+      "v0.b[0xc]": 1418,
+      "v0.b[0xd]": 1419,
+      "v0.b[0xe]": 1420,
+      "v0.b[0xf]": 1421,
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+      "v0.d[0x0]": 1423,
+      "v0.d[0x1]": 1424,
+      "v0.d[1]": 1425,
+      "v0.h": 1426,
+      "v0.h[0x0]": 1427,
+      "v0.h[0x1]": 1428,
+      "v0.h[0x2]": 1429,
+      "v0.h[0x3]": 1430,
+      "v0.h[0x4]": 1431,
+      "v0.h[0x5]": 1432,
+      "v0.h[0x6]": 1433,
+      "v0.h[0x7]": 1434,
+      "v0.s": 1435,
+      "v0.s[0x0]": 1436,
+      "v0.s[0x1]": 1437,
+      "v0.s[0x2]": 1438,
+      "v0.s[0x3]": 1439,
+      "v1.16b": 1440,
+      "v1.1d": 1441,
+      "v1.1q": 1442,
+      "v1.2d": 1443,
+      "v1.2s": 1444,
+      "v1.4h": 1445,
+      "v1.4s": 1446,
+      "v1.8b": 1447,
+      "v1.8h": 1448,
+      "v1.b": 1449,
+      "v1.b[0x0]": 1450,
+      "v1.b[0x1]": 1451,
+      "v1.b[0x2]": 1452,
+      "v1.b[0x3]": 1453,
+      "v1.b[0x4]": 1454,
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+      "{": 2749,
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+    }
+  }
+}

tokenizer_config.json

@@ -1,57 +1,18 @@
-{
-  "added_tokens_decoder": {
-    "2751": {
-      "content": "[UNK]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "2752": {
-      "content": "[SEP]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "2753": {
-      "content": "[PAD]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "2754": {
-      "content": "[CLS]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "2755": {
-      "content": "[MASK]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    }
-  },
-  "clean_up_tokenization_spaces": true,
-  "cls_token": "[CLS]",
-  "do_basic_tokenize": false,
-  "do_lower_case": false,
-  "mask_token": "[MASK]",
-  "model_max_length": 1000000000000000019884624838656,
-  "never_split": null,
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
-  "strip_accents": null,
-  "tokenize_chinese_chars": false,
-  "tokenizer_class": "ARM64Tokenizer",
-  "unk_token": "[UNK]"
-}
+{
+  "backend": "tokenizers",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": false,
+  "do_lower_case": false,
+  "is_local": false,
+  "local_files_only": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": false,
+  "tokenizer_class": "ARM64Tokenizer",
+  "unk_token": "[UNK]"
+}