manuelcaccone/modernbert-actuary-skills-classifier
Browse files- README.md +86 -0
- config.json +396 -0
- model.safetensors +3 -0
- special_tokens_map.json +37 -0
- tokenizer.json +0 -0
- tokenizer_config.json +945 -0
- training_args.bin +3 -0
README.md
ADDED
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| 1 |
+
---
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| 2 |
+
library_name: transformers
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| 3 |
+
license: apache-2.0
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| 4 |
+
base_model: answerdotai/ModernBERT-base
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+
tags:
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- generated_from_trainer
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| 7 |
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model-index:
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- name: modernbert_multilabel_model
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| 9 |
+
results: []
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| 10 |
+
---
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| 11 |
+
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| 12 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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| 13 |
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should probably proofread and complete it, then remove this comment. -->
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| 14 |
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| 15 |
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# modernbert_multilabel_model
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This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the None dataset.
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| 18 |
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It achieves the following results on the evaluation set:
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| 19 |
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- Loss: 0.0616
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- F1 Micro: 0.6728
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| 21 |
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- F1 Macro: 0.1060
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| 22 |
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- Precision Micro: 0.7915
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| 23 |
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- Precision Macro: 0.2013
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| 24 |
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- Recall Micro: 0.5850
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- Recall Macro: 0.0860
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- Hamming Loss: 0.0207
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- Exact Match Accuracy: 0.0457
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 8
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- eval_batch_size: 16
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- seed: 42
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- gradient_accumulation_steps: 2
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| 51 |
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- total_train_batch_size: 16
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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| 55 |
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- num_epochs: 10
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Precision Micro | Precision Macro | Recall Micro | Recall Macro | Hamming Loss | Exact Match Accuracy |
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| 61 |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:---------------:|:---------------:|:------------:|:------------:|:------------:|:--------------------:|
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| 0.1435 | 0.5797 | 100 | 0.0719 | 0.5833 | 0.0436 | 0.7128 | 0.0604 | 0.4936 | 0.0412 | 0.0253 | 0.0054 |
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| 0.1352 | 1.1565 | 200 | 0.0684 | 0.6069 | 0.0558 | 0.7282 | 0.0756 | 0.5202 | 0.0508 | 0.0242 | 0.0098 |
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| 64 |
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| 0.1314 | 1.7362 | 300 | 0.0643 | 0.6137 | 0.0605 | 0.8010 | 0.0911 | 0.4975 | 0.0515 | 0.0225 | 0.0098 |
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| 65 |
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| 0.1141 | 2.3130 | 400 | 0.0632 | 0.6173 | 0.0707 | 0.7935 | 0.1299 | 0.5052 | 0.0576 | 0.0225 | 0.0087 |
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| 66 |
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| 0.1153 | 2.8928 | 500 | 0.0611 | 0.6483 | 0.0697 | 0.7948 | 0.1330 | 0.5474 | 0.0585 | 0.0213 | 0.0207 |
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| 67 |
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| 0.1033 | 3.4696 | 600 | 0.0593 | 0.6649 | 0.0875 | 0.7772 | 0.1448 | 0.5810 | 0.0746 | 0.0210 | 0.0174 |
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| 68 |
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| 0.0983 | 4.0464 | 700 | 0.0589 | 0.6699 | 0.0934 | 0.7770 | 0.1504 | 0.5887 | 0.0794 | 0.0208 | 0.0229 |
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| 69 |
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| 0.0874 | 4.6261 | 800 | 0.0593 | 0.6661 | 0.0894 | 0.7864 | 0.1740 | 0.5777 | 0.0732 | 0.0208 | 0.0250 |
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| 70 |
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| 0.0771 | 5.2029 | 900 | 0.0591 | 0.6684 | 0.0968 | 0.8022 | 0.1778 | 0.5729 | 0.0783 | 0.0204 | 0.0229 |
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| 71 |
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| 0.0702 | 5.7826 | 1000 | 0.0583 | 0.6803 | 0.1035 | 0.7895 | 0.1850 | 0.5976 | 0.0845 | 0.0202 | 0.0305 |
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| 72 |
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| 0.0596 | 6.3594 | 1100 | 0.0592 | 0.6722 | 0.1043 | 0.8050 | 0.1927 | 0.5770 | 0.0833 | 0.0202 | 0.0348 |
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| 0.0563 | 6.9391 | 1200 | 0.0588 | 0.6815 | 0.1069 | 0.7962 | 0.1845 | 0.5957 | 0.0868 | 0.0200 | 0.0283 |
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| 74 |
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| 0.0461 | 7.5159 | 1300 | 0.0592 | 0.6845 | 0.1082 | 0.7849 | 0.1944 | 0.6069 | 0.0875 | 0.0201 | 0.0348 |
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| 0.0422 | 8.0928 | 1400 | 0.0599 | 0.6810 | 0.1075 | 0.7969 | 0.1916 | 0.5945 | 0.0868 | 0.0200 | 0.0348 |
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| 76 |
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| 0.0369 | 8.6725 | 1500 | 0.0602 | 0.6803 | 0.1076 | 0.7919 | 0.1948 | 0.5962 | 0.0866 | 0.0201 | 0.0359 |
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| 77 |
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| 0.0334 | 9.2493 | 1600 | 0.0601 | 0.6833 | 0.1075 | 0.7920 | 0.1939 | 0.6008 | 0.0865 | 0.0200 | 0.0370 |
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| 0.0325 | 9.8290 | 1700 | 0.0602 | 0.6809 | 0.1062 | 0.7921 | 0.1909 | 0.5971 | 0.0856 | 0.0201 | 0.0326 |
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### Framework versions
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- Transformers 4.56.1
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| 84 |
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- Pytorch 2.8.0+cu126
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| 85 |
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- Datasets 4.0.0
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| 86 |
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- Tokenizers 0.22.0
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config.json
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| 1 |
+
{
|
| 2 |
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"architectures": [
|
| 3 |
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"ModernBertForSequenceClassification"
|
| 4 |
+
],
|
| 5 |
+
"attention_bias": false,
|
| 6 |
+
"attention_dropout": 0.0,
|
| 7 |
+
"bos_token_id": null,
|
| 8 |
+
"classifier_activation": "gelu",
|
| 9 |
+
"classifier_bias": false,
|
| 10 |
+
"classifier_dropout": 0.0,
|
| 11 |
+
"classifier_pooling": "mean",
|
| 12 |
+
"cls_token_id": 50281,
|
| 13 |
+
"decoder_bias": true,
|
| 14 |
+
"deterministic_flash_attn": false,
|
| 15 |
+
"dtype": "float32",
|
| 16 |
+
"embedding_dropout": 0.0,
|
| 17 |
+
"eos_token_id": null,
|
| 18 |
+
"global_attn_every_n_layers": 3,
|
| 19 |
+
"global_rope_theta": 160000.0,
|
| 20 |
+
"gradient_checkpointing": false,
|
| 21 |
+
"hidden_activation": "gelu",
|
| 22 |
+
"hidden_size": 768,
|
| 23 |
+
"id2label": {
|
| 24 |
+
"0": "",
|
| 25 |
+
"1": "ALM",
|
| 26 |
+
"2": "AXIS",
|
| 27 |
+
"3": "Access",
|
| 28 |
+
"4": "Actuarial",
|
| 29 |
+
"5": "Actuarial Analytics",
|
| 30 |
+
"6": "Actuarial Systems",
|
| 31 |
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"7": "Actuarial Theory",
|
| 32 |
+
"8": "Alteryx",
|
| 33 |
+
"9": "Analytics",
|
| 34 |
+
"10": "Analytics Skills",
|
| 35 |
+
"11": "Banking & Finance",
|
| 36 |
+
"12": "Banking & Finance & Finance",
|
| 37 |
+
"13": "Banking & Finance Risk",
|
| 38 |
+
"14": "Bloomberg",
|
| 39 |
+
"15": "Broker Marketing",
|
| 40 |
+
"16": "Bulk Annuities",
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| 41 |
+
"17": "Bulk Bulk Annuities",
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| 42 |
+
"18": "Capital",
|
| 43 |
+
"19": "Change Consulting",
|
| 44 |
+
"20": "Claims",
|
| 45 |
+
"21": "Coding",
|
| 46 |
+
"22": "Commercial",
|
| 47 |
+
"23": "Commercial Insurance",
|
| 48 |
+
"24": "Commercial Lines",
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| 49 |
+
"25": "Commercial Property",
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| 50 |
+
"26": "Competitor Analytics",
|
| 51 |
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"27": "Compliance",
|
| 52 |
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"28": "Compliance Compliance",
|
| 53 |
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"29": "Consulting",
|
| 54 |
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"30": "Consulting Analyst",
|
| 55 |
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"31": "Consulting Analytics",
|
| 56 |
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"32": "Consulting Consulting",
|
| 57 |
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"33": "Consulting Insurance",
|
| 58 |
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"34": "Consulting P&C",
|
| 59 |
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"35": "Consulting Strategy",
|
| 60 |
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"36": "Consulting Systems",
|
| 61 |
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"37": "Consulting Systems Improvement",
|
| 62 |
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"38": "Contract",
|
| 63 |
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"39": "Crisis Consulting",
|
| 64 |
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"40": "Cyber",
|
| 65 |
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"41": "Data",
|
| 66 |
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"42": "Data Science",
|
| 67 |
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"43": "Data Science Analytics",
|
| 68 |
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"44": "Data Science Analytics Systems",
|
| 69 |
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"45": "Data Science Data Science",
|
| 70 |
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"46": "Data Science Finance",
|
| 71 |
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"47": "Data Science Risk",
|
| 72 |
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"48": "Emblem",
|
| 73 |
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"49": "Employee Pensions",
|
| 74 |
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"50": "Excel",
|
| 75 |
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|
| 76 |
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"52": "Executive Pensions",
|
| 77 |
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"53": "Experience Analytics",
|
| 78 |
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|
| 79 |
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|
| 80 |
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"56": "Finance Compliance",
|
| 81 |
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"57": "Finance Consulting",
|
| 82 |
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"58": "Finance Due Diligence",
|
| 83 |
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|
| 84 |
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"60": "Finance Instruments",
|
| 85 |
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"61": "Finance Lines",
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| 86 |
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| 87 |
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| 88 |
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| 89 |
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| 90 |
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| 91 |
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| 92 |
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|
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|
| 96 |
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| 97 |
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| 99 |
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|
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|
| 101 |
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| 102 |
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|
| 103 |
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| 104 |
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|
| 105 |
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|
| 106 |
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|
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|
| 108 |
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| 109 |
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| 111 |
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|
| 115 |
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|
| 116 |
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|
| 117 |
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"93": "London Marketing",
|
| 118 |
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"94": "Longevity",
|
| 119 |
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"95": "MATLAB",
|
| 120 |
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|
| 121 |
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"97": "Machine Learning",
|
| 122 |
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"98": "Marine",
|
| 123 |
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"99": "Marketing",
|
| 124 |
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|
| 125 |
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|
| 126 |
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|
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|
| 128 |
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|
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|
| 130 |
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|
| 131 |
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|
| 132 |
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|
| 133 |
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|
| 134 |
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|
| 135 |
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|
| 136 |
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|
| 137 |
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|
| 138 |
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|
| 139 |
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|
| 140 |
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|
| 141 |
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|
| 142 |
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|
| 143 |
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|
| 144 |
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|
| 145 |
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|
| 146 |
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|
| 147 |
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|
| 148 |
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|
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|
| 150 |
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|
| 151 |
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|
| 152 |
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|
| 153 |
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|
| 154 |
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|
| 155 |
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|
| 156 |
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|
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|
| 158 |
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|
| 159 |
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|
| 160 |
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|
| 161 |
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|
| 162 |
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|
| 163 |
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|
| 164 |
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|
| 165 |
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|
| 166 |
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|
| 167 |
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|
| 168 |
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|
| 169 |
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|
| 170 |
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|
| 171 |
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|
| 172 |
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|
| 173 |
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|
| 174 |
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|
| 175 |
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|
| 176 |
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| 177 |
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|
| 178 |
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| 179 |
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|
| 180 |
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|
| 181 |
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|
| 182 |
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|
| 183 |
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|
| 184 |
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|
| 185 |
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|
| 186 |
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|
| 187 |
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|
| 188 |
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|
| 189 |
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|
| 190 |
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|
| 191 |
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|
| 192 |
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|
| 193 |
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|
| 194 |
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|
| 195 |
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|
| 196 |
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size 598965788
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special_tokens_map.json
ADDED
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@@ -0,0 +1,37 @@
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"normalized": false,
|
| 27 |
+
"rstrip": false,
|
| 28 |
+
"single_word": false
|
| 29 |
+
},
|
| 30 |
+
"unk_token": {
|
| 31 |
+
"content": "[UNK]",
|
| 32 |
+
"lstrip": false,
|
| 33 |
+
"normalized": false,
|
| 34 |
+
"rstrip": false,
|
| 35 |
+
"single_word": false
|
| 36 |
+
}
|
| 37 |
+
}
|
tokenizer.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,945 @@
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| 1 |
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| 2 |
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| 3 |
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| 4 |
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| 18 |
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| 19 |
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| 28 |
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| 36 |
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|
| 860 |
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|
| 861 |
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|
| 862 |
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|
| 863 |
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|
| 864 |
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|
| 865 |
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|
| 866 |
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|
| 867 |
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|
| 868 |
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|
| 869 |
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|
| 870 |
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|
| 871 |
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|
| 872 |
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|
| 873 |
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|
| 874 |
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|
| 875 |
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|
| 876 |
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|
| 877 |
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|
| 878 |
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|
| 879 |
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|
| 880 |
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|
| 881 |
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|
| 882 |
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|
| 883 |
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|
| 884 |
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|
| 885 |
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|
| 886 |
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|
| 887 |
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|
| 888 |
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|
| 889 |
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|
| 890 |
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|
| 891 |
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|
| 892 |
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|
| 893 |
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|
| 894 |
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|
| 895 |
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|
| 896 |
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|
| 897 |
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|
| 898 |
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|
| 899 |
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|
| 900 |
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|
| 901 |
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|
| 902 |
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|
| 903 |
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|
| 904 |
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|
| 905 |
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|
| 906 |
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|
| 907 |
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|
| 908 |
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|
| 909 |
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|
| 910 |
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|
| 911 |
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|
| 912 |
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|
| 913 |
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|
| 914 |
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|
| 915 |
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|
| 916 |
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|
| 917 |
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|
| 918 |
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|
| 919 |
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|
| 920 |
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|
| 921 |
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|
| 922 |
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|
| 923 |
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|
| 924 |
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|
| 925 |
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|
| 926 |
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|
| 927 |
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|
| 928 |
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|
| 929 |
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|
| 930 |
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|
| 931 |
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|
| 932 |
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"clean_up_tokenization_spaces": true,
|
| 933 |
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"cls_token": "[CLS]",
|
| 934 |
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"extra_special_tokens": {},
|
| 935 |
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"mask_token": "[MASK]",
|
| 936 |
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"model_input_names": [
|
| 937 |
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"input_ids",
|
| 938 |
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"attention_mask"
|
| 939 |
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|
| 940 |
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"model_max_length": 8192,
|
| 941 |
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"pad_token": "[PAD]",
|
| 942 |
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|
| 943 |
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"tokenizer_class": "PreTrainedTokenizerFast",
|
| 944 |
+
"unk_token": "[UNK]"
|
| 945 |
+
}
|
training_args.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:112bdb9ec49945b96bbee3a925cc6e166d4ff8874983798f8ab8749bd2f20238
|
| 3 |
+
size 5777
|