Resave model

1_Pooling/config.json

@@ -1,5 +1,9 @@
-{
-    "embedding_dimension": 768,
-    "pooling_mode": "mean",
-    "include_prompt": true
+{
+  "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
 }

README.md

@@ -1,140 +1,79 @@
 ---
-tags:
-- sentence-transformers
-- sentence-similarity
-- feature-extraction
-base_model: NetherlandsForensicInstitute/ARM64BERT-embedding
-pipeline_tag: sentence-similarity
+license: eupl-1.2
+language: code
+base_model:
+- NetherlandsForensicInstitute/ARM64BERT
 library_name: sentence-transformers
 ---
 
-# 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.*
--->
+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. 

config.json

@@ -1,30 +1,25 @@
-{
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-  "is_decoder": false,
-  "layer_norm_eps": 1e-12,
-  "max_position_embeddings": 512,
-  "model_type": "bert",
-  "num_attention_heads": 12,
-  "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
-}
+{
+  "_name_or_path": "D:\\asmtransformers\\output\\models\\pretraining_mlm_2023-12-21_16-08-17",
+  "architectures": [
+    "ASMBertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
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+  "layer_norm_eps": 1e-12,
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+  "transformers_version": "4.34.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 2756
+}

config_sentence_transformers.json

@@ -1,14 +1,7 @@
-{
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-    "sentence_transformers": "5.4.1",
-    "transformers": "5.6.0"
-  },
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-  "model_type": "ASMSentenceTransformer",
-  "prompts": {
-    "document": "",
-    "query": ""
-  },
-  "similarity_fn_name": "cosine"
+{
+  "__version__": {
+    "sentence_transformers": "2.3.1",
+    "transformers": "4.34.1",
+    "pytorch": "2.1.1+cu118"
+  }
 }

model.safetensors

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modules.json

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+    "type": "sentence_transformers.models.Pooling"
+  }
 ]

sentence_bert_config.json

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-{
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+{
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+  "do_lower_case": false
 }

tokenizer.json

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-      "b22": 781,
-      "b23": 782,
-      "b24": 783,
-      "b25": 784,
-      "b26": 785,
-      "b27": 786,
-      "b28": 787,
-      "b29": 788,
-      "b3": 789,
-      "b30": 790,
-      "b31": 791,
-      "b4": 792,
-      "b5": 793,
-      "b6": 794,
-      "b7": 795,
-      "b8": 796,
-      "b9": 797,
-      "bcax": 798,
-      "bfm": 799,
-      "bfxil": 800,
-      "bic": 801,
-      "bics": 802,
-      "bif": 803,
-      "bit": 804,
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-      "blr": 806,
-      "br": 807,
-      "brk": 808,
-      "bsl": 809,
-      "bti": 810,
-      "c": 811,
-      "c0x0": 812,
-      "c0x3": 813,
-      "c0x4": 814,
-      "c0xb": 815,
-      "cas": 816,
-      "casa": 817,
-      "casab": 818,
-      "casal": 819,
-      "casalb": 820,
-      "casalh": 821,
-      "casb": 822,
-      "casl": 823,
-      "caslb": 824,
-      "caspal": 825,
-      "cbnz": 826,
-      "cbz": 827,
-      "cc": 828,
-      "ccmn": 829,
-      "ccmp": 830,
-      "cinc": 831,
-      "cinv": 832,
-      "clrex": 833,
-      "cls": 834,
-      "clz": 835,
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-      "cmge": 837,
-      "cmgt": 838,
-      "cmhi": 839,
-      "cmhs": 840,
-      "cmle": 841,
-      "cmlt": 842,
-      "cmn": 843,
-      "cmp": 844,
-      "cmtst": 845,
-      "cneg": 846,
-      "cnt": 847,
-      "cntvct_el0": 848,
-      "crc32b": 849,
-      "crc32cb": 850,
-      "crc32ch": 851,
-      "crc32cw": 852,
-      "crc32cx": 853,
-      "crc32h": 854,
-      "crc32w": 855,
-      "crc32x": 856,
-      "cs": 857,
-      "csel": 858,
-      "cset": 859,
-      "csetm": 860,
-      "csinc": 861,
-      "csinv": 862,
-      "csneg": 863,
-      "ctr_el0": 864,
-      "cvau": 865,
-      "d0": 866,
-      "d1": 867,
-      "d10": 868,
-      "d11": 869,
-      "d12": 870,
-      "d13": 871,
-      "d14": 872,
-      "d15": 873,
-      "d16": 874,
-      "d17": 875,
-      "d18": 876,
-      "d19": 877,
-      "d2": 878,
-      "d20": 879,
-      "d21": 880,
-      "d22": 881,
-      "d23": 882,
-      "d24": 883,
-      "d25": 884,
-      "d26": 885,
-      "d27": 886,
-      "d28": 887,
-      "d29": 888,
-      "d3": 889,
-      "d30": 890,
-      "d31": 891,
-      "d4": 892,
-      "d5": 893,
-      "d6": 894,
-      "d7": 895,
-      "d8": 896,
-      "d9": 897,
-      "dc": 898,
-      "dczid_el0": 899,
-      "dmb": 900,
-      "dup": 901,
-      "eon": 902,
-      "eor": 903,
-      "eor3": 904,
-      "eq": 905,
-      "ext": 906,
-      "extr": 907,
-      "fabd": 908,
-      "fabs": 909,
-      "facge": 910,
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-      "fadd": 912,
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-      "fccmp": 914,
-      "fccmpe": 915,
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-      "fcmgt": 918,
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-      "fcmlt": 920,
-      "fcmp": 921,
-      "fcmpe": 922,
-      "fcsel": 923,
-      "fcvt": 924,
-      "fcvtas": 925,
-      "fcvtau": 926,
-      "fcvtl": 927,
-      "fcvtl2": 928,
-      "fcvtms": 929,
-      "fcvtmu": 930,
-      "fcvtn": 931,
-      "fcvtn2": 932,
-      "fcvtns": 933,
-      "fcvtps": 934,
-      "fcvtpu": 935,
-      "fcvtzs": 936,
-      "fcvtzu": 937,
-      "fdiv": 938,
-      "fmadd": 939,
-      "fmax": 940,
-      "fmaxnm": 941,
-      "fmaxnmp": 942,
-      "fmaxnmv": 943,
-      "fmaxv": 944,
-      "fmin": 945,
-      "fminnm": 946,
-      "fminnmp": 947,
-      "fminnmv": 948,
-      "fmla": 949,
-      "fmls": 950,
-      "fmov": 951,
-      "fmsub": 952,
-      "fmul": 953,
-      "fneg": 954,
-      "fnmadd": 955,
-      "fnmls": 956,
-      "fnmsub": 957,
-      "fnmul": 958,
-      "fpcr": 959,
-      "fpsr": 960,
-      "frecpe": 961,
-      "frecps": 962,
-      "frinta": 963,
-      "frinti": 964,
-      "frintm": 965,
-      "frintn": 966,
-      "frintp": 967,
-      "frintx": 968,
-      "frintz": 969,
-      "frsqrte": 970,
-      "frsqrts": 971,
-      "fsqrt": 972,
-      "fsub": 973,
-      "ge": 974,
-      "gt": 975,
-      "h0": 976,
-      "h1": 977,
-      "h10": 978,
-      "h11": 979,
-      "h12": 980,
-      "h13": 981,
-      "h14": 982,
-      "h15": 983,
-      "h16": 984,
-      "h17": 985,
-      "h18": 986,
-      "h19": 987,
-      "h2": 988,
-      "h20": 989,
-      "h21": 990,
-      "h22": 991,
-      "h23": 992,
-      "h24": 993,
-      "h25": 994,
-      "h26": 995,
-      "h27": 996,
-      "h28": 997,
-      "h29": 998,
-      "h3": 999,
-      "h30": 1000,
-      "h31": 1001,
-      "h4": 1002,
-      "h5": 1003,
-      "h6": 1004,
-      "h7": 1005,
-      "h8": 1006,
-      "h9": 1007,
-      "hi": 1008,
-      "ic": 1009,
-      "id_aa64isar0_el1": 1010,
-      "id_aa64isar1_el1": 1011,
-      "id_aa64pfr0_el1": 1012,
-      "id_aa64pfr1_el1": 1013,
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-      "ish": 1015,
-      "ishld": 1016,
-      "ishst": 1017,
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-      "ld1r": 1020,
-      "ld2": 1021,
-      "ld2r": 1022,
-      "ld3": 1023,
-      "ld3r": 1024,
-      "ld4": 1025,
-      "ld4r": 1026,
-      "ldadd": 1027,
-      "ldadda": 1028,
-      "ldaddal": 1029,
-      "ldaddalb": 1030,
-      "ldaddalh": 1031,
-      "ldaddl": 1032,
-      "ldaddlb": 1033,
-      "ldar": 1034,
-      "ldarb": 1035,
-      "ldarh": 1036,
-      "ldaxp": 1037,
-      "ldaxr": 1038,
-      "ldaxrb": 1039,
-      "ldaxrh": 1040,
-      "ldclr": 1041,
-      "ldclral": 1042,
-      "ldclralb": 1043,
-      "ldclralh": 1044,
-      "ldclrb": 1045,
-      "ldclrl": 1046,
-      "ldeoral": 1047,
-      "ldeoralb": 1048,
-      "ldeoralh": 1049,
-      "ldnp": 1050,
-      "ldp": 1051,
-      "ldpsw": 1052,
-      "ldr": 1053,
-      "ldrb": 1054,
-      "ldrh": 1055,
-      "ldrsb": 1056,
-      "ldrsh": 1057,
-      "ldrsw": 1058,
-      "ldset": 1059,
-      "ldseta": 1060,
-      "ldsetal": 1061,
-      "ldsetalb": 1062,
-      "ldsetalh": 1063,
-      "ldsetb": 1064,
-      "ldsetl": 1065,
-      "ldur": 1066,
-      "ldurb": 1067,
-      "ldurh": 1068,
-      "ldursb": 1069,
-      "ldursh": 1070,
-      "ldursw": 1071,
-      "ldxp": 1072,
-      "ldxr": 1073,
-      "ldxrb": 1074,
-      "ldxrh": 1075,
-      "le": 1076,
-      "ls": 1077,
-      "lsl": 1078,
-      "lsr": 1079,
-      "lt": 1080,
-      "madd": 1081,
-      "mdccsr_el0": 1082,
-      "mi": 1083,
-      "midr_el1": 1084,
-      "mla": 1085,
-      "mls": 1086,
-      "mneg": 1087,
-      "mov": 1088,
-      "movi": 1089,
-      "movk": 1090,
-      "mrs": 1091,
-      "msl": 1092,
-      "msr": 1093,
-      "msub": 1094,
-      "mul": 1095,
-      "mvn": 1096,
-      "mvni": 1097,
-      "ne": 1098,
-      "neg": 1099,
-      "negs": 1100,
-      "ngc": 1101,
-      "ngcs": 1102,
-      "nop": 1103,
-      "nzcv": 1104,
-      "orn": 1105,
-      "orr": 1106,
-      "p0.b": 1107,
-      "p10/z": 1108,
-      "p3/m": 1109,
-      "p4/m": 1110,
-      "p5.b": 1111,
-      "p9.b": 1112,
-      "paciasp": 1113,
-      "pl": 1114,
-      "pldl1keep": 1115,
-      "pldl1strm": 1116,
-      "pldl2keep": 1117,
-      "pldl3keep": 1118,
-      "pmull": 1119,
-      "pmull2": 1120,
-      "prfm": 1121,
-      "pstl1keep": 1122,
-      "pstl1strm": 1123,
-      "q0": 1124,
-      "q1": 1125,
-      "q10": 1126,
-      "q11": 1127,
-      "q12": 1128,
-      "q13": 1129,
-      "q14": 1130,
-      "q15": 1131,
-      "q16": 1132,
-      "q17": 1133,
-      "q18": 1134,
-      "q19": 1135,
-      "q2": 1136,
-      "q20": 1137,
-      "q21": 1138,
-      "q22": 1139,
-      "q23": 1140,
-      "q24": 1141,
-      "q25": 1142,
-      "q26": 1143,
-      "q27": 1144,
-      "q28": 1145,
-      "q29": 1146,
-      "q3": 1147,
-      "q30": 1148,
-      "q31": 1149,
-      "q4": 1150,
-      "q5": 1151,
-      "q6": 1152,
-      "q7": 1153,
-      "q8": 1154,
-      "q9": 1155,
-      "raddhn": 1156,
-      "raddhn2": 1157,
-      "rax1": 1158,
-      "rbit": 1159,
-      "ret": 1160,
-      "rev": 1161,
-      "rev16": 1162,
-      "rev32": 1163,
-      "rev64": 1164,
-      "ror": 1165,
-      "rshrn": 1166,
-      "rshrn2": 1167,
-      "rsubhn2": 1168,
-      "s0": 1169,
-      "s1": 1170,
-      "s10": 1171,
-      "s11": 1172,
-      "s12": 1173,
-      "s13": 1174,
-      "s14": 1175,
-      "s15": 1176,
-      "s16": 1177,
-      "s17": 1178,
-      "s18": 1179,
-      "s19": 1180,
-      "s2": 1181,
-      "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,
-      "s9": 1200,
-      "saba": 1201,
-      "sabal": 1202,
-      "sabal2": 1203,
-      "sabd": 1204,
-      "sabdl": 1205,
-      "sadalp": 1206,
-      "saddl": 1207,
-      "saddl2": 1208,
-      "saddlp": 1209,
-      "saddlv": 1210,
-      "saddw": 1211,
-      "saddw2": 1212,
-      "sbc": 1213,
-      "sbcs": 1214,
-      "sbfiz": 1215,
-      "sbfx": 1216,
-      "scvtf": 1217,
-      "sdiv": 1218,
-      "sha1c": 1219,
-      "sha1h": 1220,
-      "sha1m": 1221,
-      "sha1p": 1222,
-      "sha1su0": 1223,
-      "sha1su1": 1224,
-      "sha256h": 1225,
-      "sha256h2": 1226,
-      "sha256su0": 1227,
-      "sha256su1": 1228,
-      "sha512h": 1229,
-      "sha512h2": 1230,
-      "sha512su0": 1231,
-      "sha512su1": 1232,
-      "shadd": 1233,
-      "shl": 1234,
-      "shll": 1235,
-      "shll2": 1236,
-      "shrn": 1237,
-      "shrn2": 1238,
-      "sli": 1239,
-      "smaddl": 1240,
-      "smax": 1241,
-      "smaxp": 1242,
-      "smaxv": 1243,
-      "smin": 1244,
-      "sminp": 1245,
-      "sminv": 1246,
-      "smlal": 1247,
-      "smlal2": 1248,
-      "smlsl": 1249,
-      "smlsl2": 1250,
-      "smnegl": 1251,
-      "smov": 1252,
-      "smsubl": 1253,
-      "smulh": 1254,
-      "smull": 1255,
-      "smull2": 1256,
-      "sp": 1257,
-      "sqadd": 1258,
-      "sqdmulh": 1259,
-      "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,
-      "ssra": 1281,
-      "ssubl": 1282,
-      "ssubl2": 1283,
-      "ssubw": 1284,
-      "ssubw2": 1285,
-      "st": 1286,
-      "st1": 1287,
-      "st2": 1288,
-      "st3": 1289,
-      "st4": 1290,
-      "stlr": 1291,
-      "stlrb": 1292,
-      "stlrh": 1293,
-      "stlxp": 1294,
-      "stlxr": 1295,
-      "stlxrb": 1296,
-      "stlxrh": 1297,
-      "stnp": 1298,
-      "stp": 1299,
-      "str": 1300,
-      "strb": 1301,
-      "strh": 1302,
-      "stur": 1303,
-      "sturb": 1304,
-      "sturh": 1305,
-      "stxp": 1306,
-      "stxr": 1307,
-      "stxrb": 1308,
-      "stxrh": 1309,
-      "sub": 1310,
-      "subs": 1311,
-      "svc": 1312,
-      "swp": 1313,
-      "swpa": 1314,
-      "swpab": 1315,
-      "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,
-      "trn1": 1331,
-      "trn2": 1332,
-      "tst": 1333,
-      "uabal": 1334,
-      "uabal2": 1335,
-      "uabd": 1336,
-      "uabdl": 1337,
-      "uabdl2": 1338,
-      "uadalp": 1339,
-      "uaddl": 1340,
-      "uaddl2": 1341,
-      "uaddlp": 1342,
-      "uaddlv": 1343,
-      "uaddw": 1344,
-      "uaddw2": 1345,
-      "ubfiz": 1346,
-      "ubfx": 1347,
-      "ucvtf": 1348,
-      "udf": 1349,
-      "udiv": 1350,
-      "udot": 1351,
-      "uhadd": 1352,
-      "umaddl": 1353,
-      "umax": 1354,
-      "umaxp": 1355,
-      "umaxv": 1356,
-      "umin": 1357,
-      "uminp": 1358,
-      "uminv": 1359,
-      "umlal": 1360,
-      "umlal2": 1361,
-      "umlsl": 1362,
-      "umnegl": 1363,
-      "umov": 1364,
-      "umsubl": 1365,
-      "umulh": 1366,
-      "umull": 1367,
-      "umull2": 1368,
-      "uqadd": 1369,
-      "uqrshrn": 1370,
-      "uqrshrn2": 1371,
-      "uqshl": 1372,
-      "uqshrn": 1373,
-      "uqshrn2": 1374,
-      "uqsub": 1375,
-      "uqxtn": 1376,
-      "urhadd": 1377,
-      "urshr": 1378,
-      "ushl": 1379,
-      "ushll": 1380,
-      "ushll2": 1381,
-      "ushr": 1382,
-      "usra": 1383,
-      "usubl": 1384,
-      "usubl2": 1385,
-      "usubw": 1386,
-      "usubw2": 1387,
-      "uxtb": 1388,
-      "uxth": 1389,
-      "uxtl": 1390,
-      "uxtl2": 1391,
-      "uxtw": 1392,
-      "uxtx": 1393,
-      "uzp1": 1394,
-      "uzp2": 1395,
-      "v0.16b": 1396,
-      "v0.1d": 1397,
-      "v0.1q": 1398,
-      "v0.2d": 1399,
-      "v0.2s": 1400,
-      "v0.4h": 1401,
-      "v0.4s": 1402,
-      "v0.8b": 1403,
-      "v0.8h": 1404,
-      "v0.b": 1405,
-      "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,
-      "v0.d": 1422,
-      "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,
-      "v1.b[0x5]": 1455,
-      "v1.b[0x6]": 1456,
-      "v1.b[0x7]": 1457,
-      "v1.b[0x8]": 1458,
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-}

tokenizer_config.json

@@ -1,18 +1,57 @@
-{
-  "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]"
-}
+{
+  "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]"
+}