Token Classification
Transformers.js
ONNX
English
bert
slot-filling
ner
quantized
Eval Results (legacy)
Instructions to use jottypro/notes-slots with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use jottypro/notes-slots with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('token-classification', 'jottypro/notes-slots');
Upload folder using huggingface_hub
Browse files- config.json +47 -0
- manifest.json +61 -0
- onnx/model_quantized.onnx +3 -0
- regression_metrics.json +79 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +58 -0
- transitions.json +114 -0
- vocab.txt +0 -0
config.json
ADDED
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@@ -0,0 +1,47 @@
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{
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"architectures": [
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"BertForTokenClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"dtype": "float32",
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| 8 |
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 256,
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"id2label": {
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"0": "O",
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"1": "B-PARTICIPANT",
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"2": "I-PARTICIPANT",
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"3": "B-PRIORITY",
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"4": "I-PRIORITY",
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"5": "B-DATETIME",
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"6": "I-DATETIME",
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"7": "B-RECURRENCE",
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"8": "I-RECURRENCE"
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},
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"initializer_range": 0.02,
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"intermediate_size": 1024,
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"label2id": {
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"B-DATETIME": 5,
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"B-PARTICIPANT": 1,
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"B-PRIORITY": 3,
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"B-RECURRENCE": 7,
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"I-DATETIME": 6,
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"I-PARTICIPANT": 2,
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"I-PRIORITY": 4,
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| 33 |
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"I-RECURRENCE": 8,
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| 34 |
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"O": 0
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},
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| 36 |
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"layer_norm_eps": 1e-12,
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| 37 |
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"max_position_embeddings": 512,
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| 38 |
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"model_type": "bert",
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| 39 |
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"num_attention_heads": 8,
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| 40 |
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"num_hidden_layers": 6,
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| 41 |
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"pad_token_id": 0,
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| 42 |
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"position_embedding_type": "absolute",
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| 43 |
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"transformers_version": "4.57.6",
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| 44 |
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"type_vocab_size": 2,
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| 45 |
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"use_cache": true,
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"vocab_size": 30522
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}
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manifest.json
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{
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"base_model": "microsoft/xtremedistil-l6-h256-uncased",
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| 3 |
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"bundle_size_mb": 13.32,
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| 4 |
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"created_at": "2026-05-03T11:46:52.122552+00:00",
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"files": {
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"config.json": "a42323a8b73212169370a0265018350775bc8556e795c3e3348293ce877f0c26",
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| 7 |
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"onnx/model_quantized.onnx": "0b9edd8df5baa28b4a6e4e7b07c796234ab2e19280e38eb7e1705d09462f143f",
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| 8 |
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"special_tokens_map.json": "b6d346be366a7d1d48332dbc9fdf3bf8960b5d879522b7799ddba59e76237ee3",
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"tokenizer.json": "79ea220416a57ca8acdf069d71875d5d2ddadef66021aeb6c120a7c546c04344",
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| 10 |
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"tokenizer_config.json": "e711904cac23112776b678356ccf702cf934babaa01125f698ac43bf9ad38e73",
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| 11 |
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"transitions.json": "ac4f0e2f0ca4dbad13ab41301354ae72dd19500299034c66fda701dcd4f8c920",
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"vocab.txt": "07eced375cec144d27c900241f3e339478dec958f92fddbc551f295c992038a3"
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},
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"model_version": "0.1.0",
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"quantization": {
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"method": "dynamic",
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"op_types_to_quantize": [
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"MatMul",
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"Gather"
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],
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"per_channel": true,
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"weight_type": "QInt8"
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},
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"quantized_source": "model_quantized.onnx",
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"quantized_test_metrics": {
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| 26 |
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"DATETIME_f1": 0.6723507917174177,
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| 27 |
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"PARTICIPANT_f1": 0.8943089430894308,
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| 28 |
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"PRIORITY_f1": 0.631578947368421,
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| 29 |
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"RECURRENCE_f1": 0.7093023255813952,
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"accuracy": 0.9049765258215963,
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| 31 |
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"f1": 0.7391569945021381,
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| 32 |
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"n_examples": 559,
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"precision": 0.8717579250720461,
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"quantization_method": "dynamic",
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"recall": 0.6415694591728526
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},
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"runtime": {
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| 38 |
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"device": "wasm",
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"dtype": "q8",
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| 40 |
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"transformers_js": "v4"
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| 41 |
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},
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"schema_version": "slot-labels-v0.3.0",
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"task": "token-classification",
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"test_metrics": {
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"base_model": "microsoft/xtremedistil-l6-h256-uncased",
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"epoch": 10.0,
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"seed": 42,
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| 48 |
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"test_DATETIME_f1": 0.8190854870775347,
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| 49 |
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"test_PARTICIPANT_f1": 0.920754716981132,
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| 50 |
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"test_PRIORITY_f1": 0.7979274611398963,
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| 51 |
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"test_RECURRENCE_f1": 0.8981481481481483,
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"test_accuracy": 0.9550178377539941,
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| 53 |
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"test_f1": 0.853470437017995,
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| 54 |
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"test_loss": 0.6731534600257874,
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| 55 |
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"test_precision": 0.8283433133732535,
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| 56 |
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"test_recall": 0.88016967126193,
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| 57 |
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"test_runtime": 1.0824,
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| 58 |
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"test_samples_per_second": 516.459,
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| 59 |
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"test_steps_per_second": 8.315
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}
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}
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onnx/model_quantized.onnx
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:0b9edd8df5baa28b4a6e4e7b07c796234ab2e19280e38eb7e1705d09462f143f
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| 3 |
+
size 13018836
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regression_metrics.json
ADDED
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@@ -0,0 +1,79 @@
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{
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| 2 |
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"focus_texts": [
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| 3 |
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"remind me to send the invoice to Yusuf tomorrow morning",
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| 4 |
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"book dentist appointment next Tuesday at 11",
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| 5 |
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"someday review ideas with @marta_notes"
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| 6 |
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],
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| 7 |
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"n_cases": 3,
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| 8 |
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"n_failed": 0,
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| 9 |
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"n_passed": 3,
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| 10 |
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"results": [
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| 11 |
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{
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| 12 |
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"missing_entities": [],
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| 13 |
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"passed": true,
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| 14 |
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"predicted_entities": [
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| 15 |
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{
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| 16 |
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"group": "PARTICIPANT",
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| 17 |
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"text": "yusuf"
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| 18 |
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},
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| 19 |
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{
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| 20 |
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"group": "DATETIME",
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| 21 |
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"text": "tomorrow morning"
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| 22 |
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}
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| 23 |
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],
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| 24 |
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"required_entities": [
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| 25 |
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{
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| 26 |
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"group": "PARTICIPANT",
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| 27 |
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"text": "Yusuf"
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| 28 |
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},
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| 29 |
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{
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| 30 |
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"group": "DATETIME",
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| 31 |
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"text": "tomorrow morning"
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| 32 |
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}
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| 33 |
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],
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| 34 |
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"text": "remind me to send the invoice to Yusuf tomorrow morning"
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| 35 |
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},
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| 36 |
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{
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| 37 |
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"missing_entities": [],
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| 38 |
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"passed": true,
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| 39 |
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"predicted_entities": [
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| 40 |
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{
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| 41 |
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"group": "DATETIME",
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| 42 |
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"text": "next tuesday at 11"
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| 43 |
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}
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| 44 |
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],
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| 45 |
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"required_entities": [
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| 46 |
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{
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| 47 |
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"group": "DATETIME",
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| 48 |
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"text": "next Tuesday at 11"
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| 49 |
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}
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| 50 |
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],
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| 51 |
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"text": "book dentist appointment next Tuesday at 11"
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| 52 |
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},
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| 53 |
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{
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| 54 |
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"missing_entities": [],
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| 55 |
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"passed": true,
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| 56 |
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"predicted_entities": [
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| 57 |
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{
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| 58 |
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"group": "PRIORITY",
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| 59 |
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"text": "someday"
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| 60 |
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},
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| 61 |
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{
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| 62 |
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"group": "PARTICIPANT",
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| 63 |
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"text": "@marta_notes"
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| 64 |
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}
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| 65 |
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],
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| 66 |
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"required_entities": [
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| 67 |
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{
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| 68 |
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"group": "PRIORITY",
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| 69 |
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"text": "someday"
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| 70 |
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},
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| 71 |
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{
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| 72 |
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"group": "PARTICIPANT",
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| 73 |
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"text": "@marta_notes"
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| 74 |
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}
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| 75 |
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],
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| 76 |
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"text": "someday review ideas with @marta_notes"
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| 77 |
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}
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| 78 |
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]
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| 79 |
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}
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special_tokens_map.json
ADDED
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{
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| 2 |
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"cls_token": "[CLS]",
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| 3 |
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"mask_token": "[MASK]",
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| 4 |
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"pad_token": "[PAD]",
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| 5 |
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"sep_token": "[SEP]",
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| 6 |
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"unk_token": "[UNK]"
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| 7 |
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}
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tokenizer.json
ADDED
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tokenizer_config.json
ADDED
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{
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| 2 |
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"added_tokens_decoder": {
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| 3 |
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"0": {
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| 4 |
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"content": "[PAD]",
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| 5 |
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"lstrip": false,
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| 6 |
+
"normalized": false,
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| 7 |
+
"rstrip": false,
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| 8 |
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"single_word": false,
|
| 9 |
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"special": true
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| 10 |
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},
|
| 11 |
+
"100": {
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| 12 |
+
"content": "[UNK]",
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| 13 |
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"lstrip": false,
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| 14 |
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"normalized": false,
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| 15 |
+
"rstrip": false,
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| 16 |
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"single_word": false,
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| 17 |
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"special": true
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| 18 |
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},
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| 19 |
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"101": {
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| 20 |
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"content": "[CLS]",
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| 21 |
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"lstrip": false,
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| 22 |
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"normalized": false,
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| 23 |
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"rstrip": false,
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| 24 |
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"single_word": false,
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| 25 |
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"special": true
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| 26 |
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},
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| 27 |
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"102": {
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| 28 |
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"content": "[SEP]",
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| 29 |
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"lstrip": false,
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| 30 |
+
"normalized": false,
|
| 31 |
+
"rstrip": false,
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| 32 |
+
"single_word": false,
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| 33 |
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"special": true
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| 34 |
+
},
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| 35 |
+
"103": {
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| 36 |
+
"content": "[MASK]",
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| 37 |
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"lstrip": false,
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| 38 |
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"normalized": false,
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| 39 |
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"rstrip": false,
|
| 40 |
+
"single_word": false,
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| 41 |
+
"special": true
|
| 42 |
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}
|
| 43 |
+
},
|
| 44 |
+
"clean_up_tokenization_spaces": true,
|
| 45 |
+
"cls_token": "[CLS]",
|
| 46 |
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"do_basic_tokenize": true,
|
| 47 |
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"do_lower_case": true,
|
| 48 |
+
"extra_special_tokens": {},
|
| 49 |
+
"mask_token": "[MASK]",
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| 50 |
+
"model_max_length": 1000000000000000019884624838656,
|
| 51 |
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"never_split": null,
|
| 52 |
+
"pad_token": "[PAD]",
|
| 53 |
+
"sep_token": "[SEP]",
|
| 54 |
+
"strip_accents": null,
|
| 55 |
+
"tokenize_chinese_chars": true,
|
| 56 |
+
"tokenizer_class": "BertTokenizer",
|
| 57 |
+
"unk_token": "[UNK]"
|
| 58 |
+
}
|
transitions.json
ADDED
|
@@ -0,0 +1,114 @@
|
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|
| 1 |
+
{
|
| 2 |
+
"labels": [
|
| 3 |
+
"O",
|
| 4 |
+
"B-PARTICIPANT",
|
| 5 |
+
"I-PARTICIPANT",
|
| 6 |
+
"B-PRIORITY",
|
| 7 |
+
"I-PRIORITY",
|
| 8 |
+
"B-DATETIME",
|
| 9 |
+
"I-DATETIME",
|
| 10 |
+
"B-RECURRENCE",
|
| 11 |
+
"I-RECURRENCE"
|
| 12 |
+
],
|
| 13 |
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"log_transitions": [
|
| 14 |
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[
|
| 15 |
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|
| 16 |
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| 17 |
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| 21 |
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| 22 |
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|
| 23 |
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|
| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
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| 28 |
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| 29 |
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| 30 |
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| 31 |
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| 32 |
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|
| 33 |
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|
| 34 |
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| 35 |
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| 36 |
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| 37 |
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| 38 |
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| 45 |
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| 111 |
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|
| 112 |
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]
|
| 113 |
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]
|
| 114 |
+
}
|
vocab.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|