gentlebowl multi-train commited on
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27b1637
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Co-authored-by: NLP <multi-train@users.noreply.huggingface.co>

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2143
+ value: 93.0728601593541
2144
+ - type: cos_sim_f1
2145
+ value: 85.6727976766699
2146
+ - type: cos_sim_precision
2147
+ value: 83.02063789868667
2148
+ - type: cos_sim_recall
2149
+ value: 88.5
2150
+ - type: dot_accuracy
2151
+ value: 99.72178217821782
2152
+ - type: dot_ap
2153
+ value: 93.07287396168348
2154
+ - type: dot_f1
2155
+ value: 85.6727976766699
2156
+ - type: dot_precision
2157
+ value: 83.02063789868667
2158
+ - type: dot_recall
2159
+ value: 88.5
2160
+ - type: euclidean_accuracy
2161
+ value: 99.72178217821782
2162
+ - type: euclidean_ap
2163
+ value: 93.07285657982895
2164
+ - type: euclidean_f1
2165
+ value: 85.6727976766699
2166
+ - type: euclidean_precision
2167
+ value: 83.02063789868667
2168
+ - type: euclidean_recall
2169
+ value: 88.5
2170
+ - type: manhattan_accuracy
2171
+ value: 99.72475247524753
2172
+ - type: manhattan_ap
2173
+ value: 93.02792973059809
2174
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2175
+ value: 85.7727737973388
2176
+ - type: manhattan_precision
2177
+ value: 87.84067085953879
2178
+ - type: manhattan_recall
2179
+ value: 83.8
2180
+ - type: max_accuracy
2181
+ value: 99.72475247524753
2182
+ - type: max_ap
2183
+ value: 93.07287396168348
2184
+ - type: max_f1
2185
+ value: 85.7727737973388
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: 68.77583615550819
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: 36.151636938606956
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: 52.16607939471187
2219
+ - type: mrr
2220
+ value: 52.95172046091163
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: 31.314646669495666
2232
+ - type: cos_sim_spearman
2233
+ value: 31.83562491439455
2234
+ - type: dot_pearson
2235
+ value: 31.314590842874157
2236
+ - type: dot_spearman
2237
+ value: 31.83363065810437
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: 0.198
2249
+ - type: map_at_10
2250
+ value: 1.3010000000000002
2251
+ - type: map_at_100
2252
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2253
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2254
+ value: 20.179
2255
+ - type: map_at_3
2256
+ value: 0.528
2257
+ - type: map_at_5
2258
+ value: 0.8019999999999999
2259
+ - type: mrr_at_1
2260
+ value: 72
2261
+ - type: mrr_at_10
2262
+ value: 83.39999999999999
2263
+ - type: mrr_at_100
2264
+ value: 83.39999999999999
2265
+ - type: mrr_at_1000
2266
+ value: 83.39999999999999
2267
+ - type: mrr_at_3
2268
+ value: 81.667
2269
+ - type: mrr_at_5
2270
+ value: 83.06700000000001
2271
+ - type: ndcg_at_1
2272
+ value: 66
2273
+ - type: ndcg_at_10
2274
+ value: 58.059000000000005
2275
+ - type: ndcg_at_100
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+ value: 44.316
2277
+ - type: ndcg_at_1000
2278
+ value: 43.147000000000006
2279
+ - type: ndcg_at_3
2280
+ value: 63.815999999999995
2281
+ - type: ndcg_at_5
2282
+ value: 63.005
2283
+ - type: precision_at_1
2284
+ value: 72
2285
+ - type: precision_at_10
2286
+ value: 61.4
2287
+ - type: precision_at_100
2288
+ value: 45.62
2289
+ - type: precision_at_1000
2290
+ value: 19.866
2291
+ - type: precision_at_3
2292
+ value: 70
2293
+ - type: precision_at_5
2294
+ value: 68.8
2295
+ - type: recall_at_1
2296
+ value: 0.198
2297
+ - type: recall_at_10
2298
+ value: 1.517
2299
+ - type: recall_at_100
2300
+ value: 10.587
2301
+ - type: recall_at_1000
2302
+ value: 41.233
2303
+ - type: recall_at_3
2304
+ value: 0.573
2305
+ - type: recall_at_5
2306
+ value: 0.907
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: 1.894
2318
+ - type: map_at_10
2319
+ value: 8.488999999999999
2320
+ - type: map_at_100
2321
+ value: 14.445
2322
+ - type: map_at_1000
2323
+ value: 16.078
2324
+ - type: map_at_3
2325
+ value: 4.589
2326
+ - type: map_at_5
2327
+ value: 6.019
2328
+ - type: mrr_at_1
2329
+ value: 22.448999999999998
2330
+ - type: mrr_at_10
2331
+ value: 39.82
2332
+ - type: mrr_at_100
2333
+ value: 40.752
2334
+ - type: mrr_at_1000
2335
+ value: 40.771
2336
+ - type: mrr_at_3
2337
+ value: 34.354
2338
+ - type: mrr_at_5
2339
+ value: 37.721
2340
+ - type: ndcg_at_1
2341
+ value: 19.387999999999998
2342
+ - type: ndcg_at_10
2343
+ value: 21.563
2344
+ - type: ndcg_at_100
2345
+ value: 33.857
2346
+ - type: ndcg_at_1000
2347
+ value: 46.199
2348
+ - type: ndcg_at_3
2349
+ value: 22.296
2350
+ - type: ndcg_at_5
2351
+ value: 21.770999999999997
2352
+ - type: precision_at_1
2353
+ value: 22.448999999999998
2354
+ - type: precision_at_10
2355
+ value: 19.796
2356
+ - type: precision_at_100
2357
+ value: 7.142999999999999
2358
+ - type: precision_at_1000
2359
+ value: 1.541
2360
+ - type: precision_at_3
2361
+ value: 24.490000000000002
2362
+ - type: precision_at_5
2363
+ value: 22.448999999999998
2364
+ - type: recall_at_1
2365
+ value: 1.894
2366
+ - type: recall_at_10
2367
+ value: 14.931
2368
+ - type: recall_at_100
2369
+ value: 45.524
2370
+ - type: recall_at_1000
2371
+ value: 83.243
2372
+ - type: recall_at_3
2373
+ value: 5.712
2374
+ - type: recall_at_5
2375
+ value: 8.386000000000001
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: 71.049
2387
+ - type: ap
2388
+ value: 13.85116971310922
2389
+ - type: f1
2390
+ value: 54.37504302487686
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: 64.1312959818902
2402
+ - type: f1
2403
+ value: 64.11413877009383
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: 54.13103431861502
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: 87.327889372355
2426
+ - type: cos_sim_ap
2427
+ value: 77.42059895975699
2428
+ - type: cos_sim_f1
2429
+ value: 71.02706903250873
2430
+ - type: cos_sim_precision
2431
+ value: 69.75324344950394
2432
+ - type: cos_sim_recall
2433
+ value: 72.34828496042216
2434
+ - type: dot_accuracy
2435
+ value: 87.327889372355
2436
+ - type: dot_ap
2437
+ value: 77.4209479346677
2438
+ - type: dot_f1
2439
+ value: 71.02706903250873
2440
+ - type: dot_precision
2441
+ value: 69.75324344950394
2442
+ - type: dot_recall
2443
+ value: 72.34828496042216
2444
+ - type: euclidean_accuracy
2445
+ value: 87.327889372355
2446
+ - type: euclidean_ap
2447
+ value: 77.42096495861037
2448
+ - type: euclidean_f1
2449
+ value: 71.02706903250873
2450
+ - type: euclidean_precision
2451
+ value: 69.75324344950394
2452
+ - type: euclidean_recall
2453
+ value: 72.34828496042216
2454
+ - type: manhattan_accuracy
2455
+ value: 87.31000774870358
2456
+ - type: manhattan_ap
2457
+ value: 77.38930750711619
2458
+ - type: manhattan_f1
2459
+ value: 71.07935314027831
2460
+ - type: manhattan_precision
2461
+ value: 67.70957726295677
2462
+ - type: manhattan_recall
2463
+ value: 74.80211081794195
2464
+ - type: max_accuracy
2465
+ value: 87.327889372355
2466
+ - type: max_ap
2467
+ value: 77.42096495861037
2468
+ - type: max_f1
2469
+ value: 71.07935314027831
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: 89.58939729110878
2481
+ - type: cos_sim_ap
2482
+ value: 87.17594155025475
2483
+ - type: cos_sim_f1
2484
+ value: 79.21146953405018
2485
+ - type: cos_sim_precision
2486
+ value: 76.8918527109307
2487
+ - type: cos_sim_recall
2488
+ value: 81.67539267015707
2489
+ - type: dot_accuracy
2490
+ value: 89.58939729110878
2491
+ - type: dot_ap
2492
+ value: 87.17593963273593
2493
+ - type: dot_f1
2494
+ value: 79.21146953405018
2495
+ - type: dot_precision
2496
+ value: 76.8918527109307
2497
+ - type: dot_recall
2498
+ value: 81.67539267015707
2499
+ - type: euclidean_accuracy
2500
+ value: 89.58939729110878
2501
+ - type: euclidean_ap
2502
+ value: 87.17592466925834
2503
+ - type: euclidean_f1
2504
+ value: 79.21146953405018
2505
+ - type: euclidean_precision
2506
+ value: 76.8918527109307
2507
+ - type: euclidean_recall
2508
+ value: 81.67539267015707
2509
+ - type: manhattan_accuracy
2510
+ value: 89.62626615438352
2511
+ - type: manhattan_ap
2512
+ value: 87.16589873161546
2513
+ - type: manhattan_f1
2514
+ value: 79.25143598295348
2515
+ - type: manhattan_precision
2516
+ value: 76.39494177323712
2517
+ - type: manhattan_recall
2518
+ value: 82.32984293193716
2519
+ - type: max_accuracy
2520
+ value: 89.62626615438352
2521
+ - type: max_ap
2522
+ value: 87.17594155025475
2523
+ - type: max_f1
2524
+ value: 79.25143598295348
2525
+ duplicated_from: hkunlp/instructor-large
2526
+ ---
2527
+
2528
+ # hkunlp/instructor-large
2529
+ 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 ([MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard))!
2530
+ 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/)!
2531
+
2532
+ **************************** **Updates** ****************************
2533
+
2534
+ * 12/28: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-large) trained with hard negatives, which gives better performance.
2535
+ * 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-large) and [project page](https://instructor-embedding.github.io/)! Check them out!
2536
+
2537
+ ## Quick start
2538
+ <hr />
2539
+
2540
+ ## Installation
2541
+ ```bash
2542
+ pip install InstructorEmbedding
2543
+ ```
2544
+
2545
+ ## Compute your customized embeddings
2546
+ Then you can use the model like this to calculate domain-specific and task-aware embeddings:
2547
+ ```python
2548
+ from InstructorEmbedding import INSTRUCTOR
2549
+ model = INSTRUCTOR('hkunlp/instructor-large')
2550
+ sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
2551
+ instruction = "Represent the Science title:"
2552
+ embeddings = model.encode([[instruction,sentence]])
2553
+ print(embeddings)
2554
+ ```
2555
+
2556
+ ## Use cases
2557
+ <hr />
2558
+
2559
+ ## Calculate embeddings for your customized texts
2560
+ If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions:
2561
+
2562
+ &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`:
2563
+ * `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc.
2564
+ * `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc.
2565
+ * `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc.
2566
+
2567
+ ## Calculate Sentence similarities
2568
+ You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**.
2569
+ ```python
2570
+ from sklearn.metrics.pairwise import cosine_similarity
2571
+ sentences_a = [['Represent the Science sentence: ','Parton energy loss in QCD matter'],
2572
+ ['Represent the Financial statement: ','The Federal Reserve on Wednesday raised its benchmark interest rate.']]
2573
+ sentences_b = [['Represent the Science sentence: ','The Chiral Phase Transition in Dissipative Dynamics'],
2574
+ ['Represent the Financial statement: ','The funds rose less than 0.5 per cent on Friday']]
2575
+ embeddings_a = model.encode(sentences_a)
2576
+ embeddings_b = model.encode(sentences_b)
2577
+ similarities = cosine_similarity(embeddings_a,embeddings_b)
2578
+ print(similarities)
2579
+ ```
2580
+
2581
+ ## Information Retrieval
2582
+ You can also use **customized embeddings** for information retrieval.
2583
+ ```python
2584
+ import numpy as np
2585
+ from sklearn.metrics.pairwise import cosine_similarity
2586
+ query = [['Represent the Wikipedia question for retrieving supporting documents: ','where is the food stored in a yam plant']]
2587
+ 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.'],
2588
+ ['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"],
2589
+ ['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.']]
2590
+ query_embeddings = model.encode(query)
2591
+ corpus_embeddings = model.encode(corpus)
2592
+ similarities = cosine_similarity(query_embeddings,corpus_embeddings)
2593
+ retrieved_doc_id = np.argmax(similarities)
2594
+ print(retrieved_doc_id)
2595
+ ```
2596
+
2597
+ ## Clustering
2598
+ Use **customized embeddings** for clustering texts in groups.
2599
+ ```python
2600
+ import sklearn.cluster
2601
+ sentences = [['Represent the Medicine sentence for clustering: ','Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity'],
2602
+ ['Represent the Medicine sentence for clustering: ','Comparison of Atmospheric Neutrino Flux Calculations at Low Energies'],
2603
+ ['Represent the Medicine sentence for clustering: ','Fermion Bags in the Massive Gross-Neveu Model'],
2604
+ ['Represent the Medicine sentence for clustering: ',"QCD corrections to Associated t-tbar-H production at the Tevatron"],
2605
+ ['Represent the Medicine sentence for clustering: ','A New Analysis of the R Measurements: Resonance Parameters of the Higher, Vector States of Charmonium']]
2606
+ embeddings = model.encode(sentences)
2607
+ clustering_model = sklearn.cluster.MiniBatchKMeans(n_clusters=2)
2608
+ clustering_model.fit(embeddings)
2609
+ cluster_assignment = clustering_model.labels_
2610
+ print(cluster_assignment)
2611
+ ```
config.json ADDED
@@ -0,0 +1,60 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "/scratch/acd13578qu/metatrain_models/enhanced_large/checkpoint-300/",
3
+ "architectures": [
4
+ "T5EncoderModel"
5
+ ],
6
+ "d_ff": 4096,
7
+ "d_kv": 64,
8
+ "d_model": 1024,
9
+ "decoder_start_token_id": 0,
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+ "dense_act_fn": "relu",
11
+ "dropout_rate": 0.1,
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+ "eos_token_id": 1,
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+ "feed_forward_proj": "relu",
14
+ "initializer_factor": 1.0,
15
+ "is_encoder_decoder": true,
16
+ "is_gated_act": false,
17
+ "layer_norm_epsilon": 1e-06,
18
+ "model_type": "t5",
19
+ "n_positions": 512,
20
+ "num_decoder_layers": 24,
21
+ "num_heads": 16,
22
+ "num_layers": 24,
23
+ "output_past": true,
24
+ "pad_token_id": 0,
25
+ "relative_attention_max_distance": 128,
26
+ "relative_attention_num_buckets": 32,
27
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