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+ - type: map_at_1
2072
+ value: 44.583
2073
+ - type: map_at_10
2074
+ value: 52.978
2075
+ - type: map_at_100
2076
+ value: 53.803
2077
+ - type: map_at_1000
2078
+ value: 53.839999999999996
2079
+ - type: map_at_3
2080
+ value: 50.03300000000001
2081
+ - type: map_at_5
2082
+ value: 51.939
2083
+ - type: mrr_at_1
2084
+ value: 47.0
2085
+ - type: mrr_at_10
2086
+ value: 54.730000000000004
2087
+ - type: mrr_at_100
2088
+ value: 55.31399999999999
2089
+ - type: mrr_at_1000
2090
+ value: 55.346
2091
+ - type: mrr_at_3
2092
+ value: 52.0
2093
+ - type: mrr_at_5
2094
+ value: 53.783
2095
+ - type: ndcg_at_1
2096
+ value: 47.0
2097
+ - type: ndcg_at_10
2098
+ value: 57.82899999999999
2099
+ - type: ndcg_at_100
2100
+ value: 61.49400000000001
2101
+ - type: ndcg_at_1000
2102
+ value: 62.676
2103
+ - type: ndcg_at_3
2104
+ value: 52.373000000000005
2105
+ - type: ndcg_at_5
2106
+ value: 55.481
2107
+ - type: precision_at_1
2108
+ value: 47.0
2109
+ - type: precision_at_10
2110
+ value: 7.867
2111
+ - type: precision_at_100
2112
+ value: 0.997
2113
+ - type: precision_at_1000
2114
+ value: 0.11
2115
+ - type: precision_at_3
2116
+ value: 20.556
2117
+ - type: precision_at_5
2118
+ value: 14.066999999999998
2119
+ - type: recall_at_1
2120
+ value: 44.583
2121
+ - type: recall_at_10
2122
+ value: 71.172
2123
+ - type: recall_at_100
2124
+ value: 87.7
2125
+ - type: recall_at_1000
2126
+ value: 97.333
2127
+ - type: recall_at_3
2128
+ value: 56.511
2129
+ - type: recall_at_5
2130
+ value: 64.206
2131
+ - task:
2132
+ type: PairClassification
2133
+ dataset:
2134
+ type: mteb/sprintduplicatequestions-pairclassification
2135
+ name: MTEB SprintDuplicateQuestions
2136
+ config: default
2137
+ split: test
2138
+ revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
2139
+ metrics:
2140
+ - type: cos_sim_accuracy
2141
+ value: 99.66237623762376
2142
+ - type: cos_sim_ap
2143
+ value: 90.35465126226322
2144
+ - type: cos_sim_f1
2145
+ value: 82.44575936883628
2146
+ - type: cos_sim_precision
2147
+ value: 81.32295719844358
2148
+ - type: cos_sim_recall
2149
+ value: 83.6
2150
+ - type: dot_accuracy
2151
+ value: 99.66237623762376
2152
+ - type: dot_ap
2153
+ value: 90.35464287920453
2154
+ - type: dot_f1
2155
+ value: 82.44575936883628
2156
+ - type: dot_precision
2157
+ value: 81.32295719844358
2158
+ - type: dot_recall
2159
+ value: 83.6
2160
+ - type: euclidean_accuracy
2161
+ value: 99.66237623762376
2162
+ - type: euclidean_ap
2163
+ value: 90.3546512622632
2164
+ - type: euclidean_f1
2165
+ value: 82.44575936883628
2166
+ - type: euclidean_precision
2167
+ value: 81.32295719844358
2168
+ - type: euclidean_recall
2169
+ value: 83.6
2170
+ - type: manhattan_accuracy
2171
+ value: 99.65940594059406
2172
+ - type: manhattan_ap
2173
+ value: 90.29220174849843
2174
+ - type: manhattan_f1
2175
+ value: 82.4987605354487
2176
+ - type: manhattan_precision
2177
+ value: 81.80924287118977
2178
+ - type: manhattan_recall
2179
+ value: 83.2
2180
+ - type: max_accuracy
2181
+ value: 99.66237623762376
2182
+ - type: max_ap
2183
+ value: 90.35465126226322
2184
+ - type: max_f1
2185
+ value: 82.4987605354487
2186
+ - task:
2187
+ type: Clustering
2188
+ dataset:
2189
+ type: mteb/stackexchange-clustering
2190
+ name: MTEB StackExchangeClustering
2191
+ config: default
2192
+ split: test
2193
+ revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
2194
+ metrics:
2195
+ - type: v_measure
2196
+ value: 65.0394225901397
2197
+ - task:
2198
+ type: Clustering
2199
+ dataset:
2200
+ type: mteb/stackexchange-clustering-p2p
2201
+ name: MTEB StackExchangeClusteringP2P
2202
+ config: default
2203
+ split: test
2204
+ revision: 815ca46b2622cec33ccafc3735d572c266efdb44
2205
+ metrics:
2206
+ - type: v_measure
2207
+ value: 35.27954189859326
2208
+ - task:
2209
+ type: Reranking
2210
+ dataset:
2211
+ type: mteb/stackoverflowdupquestions-reranking
2212
+ name: MTEB StackOverflowDupQuestions
2213
+ config: default
2214
+ split: test
2215
+ revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
2216
+ metrics:
2217
+ - type: map
2218
+ value: 50.99055979974896
2219
+ - type: mrr
2220
+ value: 51.82745257193787
2221
+ - task:
2222
+ type: Summarization
2223
+ dataset:
2224
+ type: mteb/summeval
2225
+ name: MTEB SummEval
2226
+ config: default
2227
+ split: test
2228
+ revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
2229
+ metrics:
2230
+ - type: cos_sim_pearson
2231
+ value: 30.21655465344237
2232
+ - type: cos_sim_spearman
2233
+ value: 29.853205339630172
2234
+ - type: dot_pearson
2235
+ value: 30.216540628083564
2236
+ - type: dot_spearman
2237
+ value: 29.868978894753027
2238
+ - task:
2239
+ type: Retrieval
2240
+ dataset:
2241
+ type: trec-covid
2242
+ name: MTEB TRECCOVID
2243
+ config: default
2244
+ split: test
2245
+ revision: None
2246
+ metrics:
2247
+ - type: map_at_1
2248
+ value: 0.2
2249
+ - type: map_at_10
2250
+ value: 1.398
2251
+ - type: map_at_100
2252
+ value: 7.406
2253
+ - type: map_at_1000
2254
+ value: 18.401
2255
+ - type: map_at_3
2256
+ value: 0.479
2257
+ - type: map_at_5
2258
+ value: 0.772
2259
+ - type: mrr_at_1
2260
+ value: 70.0
2261
+ - type: mrr_at_10
2262
+ value: 79.25999999999999
2263
+ - type: mrr_at_100
2264
+ value: 79.25999999999999
2265
+ - type: mrr_at_1000
2266
+ value: 79.25999999999999
2267
+ - type: mrr_at_3
2268
+ value: 77.333
2269
+ - type: mrr_at_5
2270
+ value: 78.133
2271
+ - type: ndcg_at_1
2272
+ value: 63.0
2273
+ - type: ndcg_at_10
2274
+ value: 58.548
2275
+ - type: ndcg_at_100
2276
+ value: 45.216
2277
+ - type: ndcg_at_1000
2278
+ value: 41.149
2279
+ - type: ndcg_at_3
2280
+ value: 60.641999999999996
2281
+ - type: ndcg_at_5
2282
+ value: 61.135
2283
+ - type: precision_at_1
2284
+ value: 70.0
2285
+ - type: precision_at_10
2286
+ value: 64.0
2287
+ - type: precision_at_100
2288
+ value: 46.92
2289
+ - type: precision_at_1000
2290
+ value: 18.642
2291
+ - type: precision_at_3
2292
+ value: 64.667
2293
+ - type: precision_at_5
2294
+ value: 66.4
2295
+ - type: recall_at_1
2296
+ value: 0.2
2297
+ - type: recall_at_10
2298
+ value: 1.6729999999999998
2299
+ - type: recall_at_100
2300
+ value: 10.856
2301
+ - type: recall_at_1000
2302
+ value: 38.964999999999996
2303
+ - type: recall_at_3
2304
+ value: 0.504
2305
+ - type: recall_at_5
2306
+ value: 0.852
2307
+ - task:
2308
+ type: Retrieval
2309
+ dataset:
2310
+ type: webis-touche2020
2311
+ name: MTEB Touche2020
2312
+ config: default
2313
+ split: test
2314
+ revision: None
2315
+ metrics:
2316
+ - type: map_at_1
2317
+ value: 1.6629999999999998
2318
+ - type: map_at_10
2319
+ value: 8.601
2320
+ - type: map_at_100
2321
+ value: 14.354
2322
+ - type: map_at_1000
2323
+ value: 15.927
2324
+ - type: map_at_3
2325
+ value: 4.1930000000000005
2326
+ - type: map_at_5
2327
+ value: 5.655
2328
+ - type: mrr_at_1
2329
+ value: 18.367
2330
+ - type: mrr_at_10
2331
+ value: 34.466
2332
+ - type: mrr_at_100
2333
+ value: 35.235
2334
+ - type: mrr_at_1000
2335
+ value: 35.27
2336
+ - type: mrr_at_3
2337
+ value: 28.571
2338
+ - type: mrr_at_5
2339
+ value: 31.531
2340
+ - type: ndcg_at_1
2341
+ value: 14.285999999999998
2342
+ - type: ndcg_at_10
2343
+ value: 20.374
2344
+ - type: ndcg_at_100
2345
+ value: 33.532000000000004
2346
+ - type: ndcg_at_1000
2347
+ value: 45.561
2348
+ - type: ndcg_at_3
2349
+ value: 18.442
2350
+ - type: ndcg_at_5
2351
+ value: 18.076
2352
+ - type: precision_at_1
2353
+ value: 18.367
2354
+ - type: precision_at_10
2355
+ value: 20.204
2356
+ - type: precision_at_100
2357
+ value: 7.489999999999999
2358
+ - type: precision_at_1000
2359
+ value: 1.5630000000000002
2360
+ - type: precision_at_3
2361
+ value: 21.769
2362
+ - type: precision_at_5
2363
+ value: 20.408
2364
+ - type: recall_at_1
2365
+ value: 1.6629999999999998
2366
+ - type: recall_at_10
2367
+ value: 15.549
2368
+ - type: recall_at_100
2369
+ value: 47.497
2370
+ - type: recall_at_1000
2371
+ value: 84.524
2372
+ - type: recall_at_3
2373
+ value: 5.289
2374
+ - type: recall_at_5
2375
+ value: 8.035
2376
+ - task:
2377
+ type: Classification
2378
+ dataset:
2379
+ type: mteb/toxic_conversations_50k
2380
+ name: MTEB ToxicConversationsClassification
2381
+ config: default
2382
+ split: test
2383
+ revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
2384
+ metrics:
2385
+ - type: accuracy
2386
+ value: 71.8194
2387
+ - type: ap
2388
+ value: 14.447702451658554
2389
+ - type: f1
2390
+ value: 55.13659412856185
2391
+ - task:
2392
+ type: Classification
2393
+ dataset:
2394
+ type: mteb/tweet_sentiment_extraction
2395
+ name: MTEB TweetSentimentExtractionClassification
2396
+ config: default
2397
+ split: test
2398
+ revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
2399
+ metrics:
2400
+ - type: accuracy
2401
+ value: 63.310696095076416
2402
+ - type: f1
2403
+ value: 63.360434851097814
2404
+ - task:
2405
+ type: Clustering
2406
+ dataset:
2407
+ type: mteb/twentynewsgroups-clustering
2408
+ name: MTEB TwentyNewsgroupsClustering
2409
+ config: default
2410
+ split: test
2411
+ revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
2412
+ metrics:
2413
+ - type: v_measure
2414
+ value: 51.30677907335145
2415
+ - task:
2416
+ type: PairClassification
2417
+ dataset:
2418
+ type: mteb/twittersemeval2015-pairclassification
2419
+ name: MTEB TwitterSemEval2015
2420
+ config: default
2421
+ split: test
2422
+ revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
2423
+ metrics:
2424
+ - type: cos_sim_accuracy
2425
+ value: 86.12386004649221
2426
+ - type: cos_sim_ap
2427
+ value: 73.99096426215495
2428
+ - type: cos_sim_f1
2429
+ value: 68.18416968442834
2430
+ - type: cos_sim_precision
2431
+ value: 66.86960933536275
2432
+ - type: cos_sim_recall
2433
+ value: 69.55145118733509
2434
+ - type: dot_accuracy
2435
+ value: 86.12386004649221
2436
+ - type: dot_ap
2437
+ value: 73.99096813038672
2438
+ - type: dot_f1
2439
+ value: 68.18416968442834
2440
+ - type: dot_precision
2441
+ value: 66.86960933536275
2442
+ - type: dot_recall
2443
+ value: 69.55145118733509
2444
+ - type: euclidean_accuracy
2445
+ value: 86.12386004649221
2446
+ - type: euclidean_ap
2447
+ value: 73.99095984980165
2448
+ - type: euclidean_f1
2449
+ value: 68.18416968442834
2450
+ - type: euclidean_precision
2451
+ value: 66.86960933536275
2452
+ - type: euclidean_recall
2453
+ value: 69.55145118733509
2454
+ - type: manhattan_accuracy
2455
+ value: 86.09405734040651
2456
+ - type: manhattan_ap
2457
+ value: 73.96825745608601
2458
+ - type: manhattan_f1
2459
+ value: 68.13888179729383
2460
+ - type: manhattan_precision
2461
+ value: 65.99901088031652
2462
+ - type: manhattan_recall
2463
+ value: 70.42216358839049
2464
+ - type: max_accuracy
2465
+ value: 86.12386004649221
2466
+ - type: max_ap
2467
+ value: 73.99096813038672
2468
+ - type: max_f1
2469
+ value: 68.18416968442834
2470
+ - task:
2471
+ type: PairClassification
2472
+ dataset:
2473
+ type: mteb/twitterurlcorpus-pairclassification
2474
+ name: MTEB TwitterURLCorpus
2475
+ config: default
2476
+ split: test
2477
+ revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
2478
+ metrics:
2479
+ - type: cos_sim_accuracy
2480
+ value: 88.99367407924865
2481
+ - type: cos_sim_ap
2482
+ value: 86.19720829843081
2483
+ - type: cos_sim_f1
2484
+ value: 78.39889075384951
2485
+ - type: cos_sim_precision
2486
+ value: 74.5110278818144
2487
+ - type: cos_sim_recall
2488
+ value: 82.71481367416075
2489
+ - type: dot_accuracy
2490
+ value: 88.99367407924865
2491
+ - type: dot_ap
2492
+ value: 86.19718471454047
2493
+ - type: dot_f1
2494
+ value: 78.39889075384951
2495
+ - type: dot_precision
2496
+ value: 74.5110278818144
2497
+ - type: dot_recall
2498
+ value: 82.71481367416075
2499
+ - type: euclidean_accuracy
2500
+ value: 88.99367407924865
2501
+ - type: euclidean_ap
2502
+ value: 86.1972021422436
2503
+ - type: euclidean_f1
2504
+ value: 78.39889075384951
2505
+ - type: euclidean_precision
2506
+ value: 74.5110278818144
2507
+ - type: euclidean_recall
2508
+ value: 82.71481367416075
2509
+ - type: manhattan_accuracy
2510
+ value: 88.95680521597392
2511
+ - type: manhattan_ap
2512
+ value: 86.16659921351506
2513
+ - type: manhattan_f1
2514
+ value: 78.39125971550081
2515
+ - type: manhattan_precision
2516
+ value: 74.82502799552073
2517
+ - type: manhattan_recall
2518
+ value: 82.31444410224823
2519
+ - type: max_accuracy
2520
+ value: 88.99367407924865
2521
+ - type: max_ap
2522
+ value: 86.19720829843081
2523
+ - type: max_f1
2524
+ value: 78.39889075384951
2525
  ---
2526
+
2527
+ # hkunlp/instructor-base
2528
+ We introduce **Instructor**👨‍🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨‍ achieves sota on 70 diverse embedding tasks!
2529
+ The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out [our paper](https://arxiv.org/abs/2212.09741) and [project page](https://instructor-embedding.github.io/)!
2530
+
2531
+ **************************** **Updates** ****************************
2532
+
2533
+ * 01/21: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-base) trained with hard negatives, which gives better performance.
2534
+ * 12/21: We released our [paper](https://arxiv.org/abs/2212.09741), [code](https://github.com/HKUNLP/instructor-embedding), [checkpoint](https://huggingface.co/hkunlp/instructor-base) and [project page](https://instructor-embedding.github.io/)! Check them out!
2535
+
2536
+ ## Quick start
2537
+ <hr />
2538
+
2539
+ ## Installation
2540
+ ```bash
2541
+ pip install InstructorEmbedding
2542
+ ```
2543
+
2544
+ ## Compute your customized embeddings
2545
+ Then you can use the model like this to calculate domain-specific and task-aware embeddings:
2546
+ ```python
2547
+ from InstructorEmbedding import INSTRUCTOR
2548
+ model = INSTRUCTOR('hkunlp/instructor-base')
2549
+ sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
2550
+ instruction = "Represent the Science title:"
2551
+ embeddings = model.encode([[instruction,sentence]])
2552
+ print(embeddings)
2553
+ ```
2554
+
2555
+ ## Use cases
2556
+ <hr />
2557
+
2558
+ ## Calculate embeddings for your customized texts
2559
+ If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions:
2560
+
2561
+ &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Represent the `domain` `text_type` for `task_objective`:
2562
+ * `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc.
2563
+ * `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc.
2564
+ * `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc.
2565
+
2566
+ ## Calculate Sentence similarities
2567
+ You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**.
2568
+ ```python
2569
+ from sklearn.metrics.pairwise import cosine_similarity
2570
+ sentences_a = [['Represent the Science sentence: ','Parton energy loss in QCD matter'],
2571
+ ['Represent the Financial statement: ','The Federal Reserve on Wednesday raised its benchmark interest rate.']]
2572
+ sentences_b = [['Represent the Science sentence: ','The Chiral Phase Transition in Dissipative Dynamics'],
2573
+ ['Represent the Financial statement: ','The funds rose less than 0.5 per cent on Friday']]
2574
+ embeddings_a = model.encode(sentences_a)
2575
+ embeddings_b = model.encode(sentences_b)
2576
+ similarities = cosine_similarity(embeddings_a,embeddings_b)
2577
+ print(similarities)
2578
+ ```
2579
+
2580
+ ## Information Retrieval
2581
+ You can also use **customized embeddings** for information retrieval.
2582
+ ```python
2583
+ import numpy as np
2584
+ from sklearn.metrics.pairwise import cosine_similarity
2585
+ query = [['Represent the Wikipedia question for retrieving supporting documents: ','where is the food stored in a yam plant']]
2586
+ corpus = [['Represent the Wikipedia document for retrieval: ','Capitalism has been dominant in the Western world since the end of feudalism, but most feel[who?] that the term "mixed economies" more precisely describes most contemporary economies, due to their containing both private-owned and state-owned enterprises. In capitalism, prices determine the demand-supply scale. For example, higher demand for certain goods and services lead to higher prices and lower demand for certain goods lead to lower prices.'],
2587
+ ['Represent the Wikipedia document for retrieval: ',"The disparate impact theory is especially controversial under the Fair Housing Act because the Act regulates many activities relating to housing, insurance, and mortgage loans—and some scholars have argued that the theory's use under the Fair Housing Act, combined with extensions of the Community Reinvestment Act, contributed to rise of sub-prime lending and the crash of the U.S. housing market and ensuing global economic recession"],
2588
+ ['Represent the Wikipedia document for retrieval: ','Disparate impact in United States labor law refers to practices in employment, housing, and other areas that adversely affect one group of people of a protected characteristic more than another, even though rules applied by employers or landlords are formally neutral. Although the protected classes vary by statute, most federal civil rights laws protect based on race, color, religion, national origin, and sex as protected traits, and some laws include disability status and other traits as well.']]
2589
+ query_embeddings = model.encode(query)
2590
+ corpus_embeddings = model.encode(corpus)
2591
+ similarities = cosine_similarity(query_embeddings,corpus_embeddings)
2592
+ retrieved_doc_id = np.argmax(similarities)
2593
+ print(retrieved_doc_id)
2594
+ ```
2595
+
2596
+ ## Clustering
2597
+ Use **customized embeddings** for clustering texts in groups.
2598
+ ```python
2599
+ import sklearn.cluster
2600
+ sentences = [['Represent the Medicine sentence for clustering: ','Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity'],
2601
+ ['Represent the Medicine sentence for clustering: ','Comparison of Atmospheric Neutrino Flux Calculations at Low Energies'],
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+ ['Represent the Medicine sentence for clustering: ','Fermion Bags in the Massive Gross-Neveu Model'],
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+ ['Represent the Medicine sentence for clustering: ',"QCD corrections to Associated t-tbar-H production at the Tevatron"],
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+ ['Represent the Medicine sentence for clustering: ','A New Analysis of the R Measurements: Resonance Parameters of the Higher, Vector States of Charmonium']]
2605
+ embeddings = model.encode(sentences)
2606
+ clustering_model = sklearn.cluster.MiniBatchKMeans(n_clusters=2)
2607
+ clustering_model.fit(embeddings)
2608
+ cluster_assignment = clustering_model.labels_
2609
+ print(cluster_assignment)
2610
+ ```
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tokenizer_config.json ADDED
@@ -0,0 +1,112 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "additional_special_tokens": [
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+ "<extra_id_0>",
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+ "<extra_id_1>",
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+ "<extra_id_2>",
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+ "<extra_id_3>",
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+ "<extra_id_4>",
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+ "<extra_id_5>",
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+ "<extra_id_6>",
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+ "<extra_id_7>",
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+ "<extra_id_8>",
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+ "<extra_id_9>",
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+ "<extra_id_10>",
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+ "<extra_id_11>",
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+ "<extra_id_12>",
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+ "<extra_id_13>",
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+ "<extra_id_14>",
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+ "<extra_id_15>",
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+ "<extra_id_16>",
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+ "<extra_id_17>",
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+ "<extra_id_18>",
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+ "<extra_id_19>",
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+ "<extra_id_20>",
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+ "<extra_id_21>",
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+ "<extra_id_22>",
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+ "<extra_id_23>",
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+ "<extra_id_24>",
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+ "<extra_id_25>",
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+ "<extra_id_26>",
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+ "<extra_id_27>",
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+ "<extra_id_28>",
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+ "<extra_id_29>",
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+ "<extra_id_30>",
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+ "<extra_id_31>",
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+ "<extra_id_32>",
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+ "<extra_id_33>",
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+ "<extra_id_34>",
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+ "<extra_id_35>",
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+ "<extra_id_36>",
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+ "<extra_id_37>",
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+ "<extra_id_38>",
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+ "<extra_id_39>",
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+ "<extra_id_40>",
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+ "<extra_id_41>",
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+ "<extra_id_42>",
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+ "<extra_id_43>",
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+ "<extra_id_44>",
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+ "<extra_id_45>",
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+ "<extra_id_46>",
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+ "<extra_id_47>",
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+ "<extra_id_48>",
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+ "<extra_id_49>",
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+ "<extra_id_50>",
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+ "<extra_id_51>",
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+ "<extra_id_52>",
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+ "<extra_id_53>",
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+ "<extra_id_54>",
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+ "<extra_id_55>",
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+ "<extra_id_56>",
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+ "<extra_id_57>",
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+ "<extra_id_58>",
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+ "<extra_id_59>",
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+ "<extra_id_60>",
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+ "<extra_id_61>",
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+ "<extra_id_62>",
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+ "<extra_id_63>",
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+ "<extra_id_64>",
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+ "<extra_id_65>",
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+ "<extra_id_66>",
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+ "<extra_id_67>",
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+ "<extra_id_68>",
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+ "<extra_id_69>",
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+ "<extra_id_70>",
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+ "<extra_id_71>",
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+ "<extra_id_72>",
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+ "<extra_id_73>",
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+ "<extra_id_74>",
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+ "<extra_id_75>",
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+ "<extra_id_76>",
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+ "<extra_id_77>",
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+ "<extra_id_78>",
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+ "<extra_id_79>",
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+ "<extra_id_80>",
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+ "<extra_id_81>",
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+ "<extra_id_82>",
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+ "<extra_id_83>",
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+ "<extra_id_84>",
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+ "<extra_id_85>",
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+ "<extra_id_86>",
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+ "<extra_id_87>",
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+ "<extra_id_88>",
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+ "<extra_id_89>",
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+ "<extra_id_90>",
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+ "<extra_id_91>",
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+ "<extra_id_92>",
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+ "<extra_id_93>",
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+ "<extra_id_94>",
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+ "<extra_id_95>",
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+ "<extra_id_96>",
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+ "<extra_id_97>",
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+ "<extra_id_98>",
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+ "<extra_id_99>"
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+ ],
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+ "eos_token": "</s>",
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+ "extra_ids": 100,
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+ "model_max_length": 512,
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+ "name_or_path": "./cache/hkunlp_instructor-base/",
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+ "pad_token": "<pad>",
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+ "special_tokens_map_file": null,
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+ "tokenizer_class": "T5Tokenizer",
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+ "unk_token": "<unk>"
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+ }