michael-newsrx-com multi-train commited on
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7c76918
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Duplicate from hkunlp/instructor-xl

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Co-authored-by: NLP <multi-train@users.noreply.huggingface.co>

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+ value: 99.79009900990098
2142
+ - type: cos_sim_ap
2143
+ value: 94.94115052608419
2144
+ - type: cos_sim_f1
2145
+ value: 89.1260162601626
2146
+ - type: cos_sim_precision
2147
+ value: 90.599173553719
2148
+ - type: cos_sim_recall
2149
+ value: 87.7
2150
+ - type: dot_accuracy
2151
+ value: 99.79009900990098
2152
+ - type: dot_ap
2153
+ value: 94.94115052608419
2154
+ - type: dot_f1
2155
+ value: 89.1260162601626
2156
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2157
+ value: 90.599173553719
2158
+ - type: dot_recall
2159
+ value: 87.7
2160
+ - type: euclidean_accuracy
2161
+ value: 99.79009900990098
2162
+ - type: euclidean_ap
2163
+ value: 94.94115052608419
2164
+ - type: euclidean_f1
2165
+ value: 89.1260162601626
2166
+ - type: euclidean_precision
2167
+ value: 90.599173553719
2168
+ - type: euclidean_recall
2169
+ value: 87.7
2170
+ - type: manhattan_accuracy
2171
+ value: 99.7940594059406
2172
+ - type: manhattan_ap
2173
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2174
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2175
+ value: 89.24508790072387
2176
+ - type: manhattan_precision
2177
+ value: 92.3982869379015
2178
+ - type: manhattan_recall
2179
+ value: 86.3
2180
+ - type: max_accuracy
2181
+ value: 99.7940594059406
2182
+ - type: max_ap
2183
+ value: 94.95271414642431
2184
+ - type: max_f1
2185
+ value: 89.24508790072387
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: 68.43866571935851
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: 35.16579026551532
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: 52.518952473513934
2219
+ - type: mrr
2220
+ value: 53.292457134368895
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: 31.12529588316604
2232
+ - type: cos_sim_spearman
2233
+ value: 32.31662126895294
2234
+ - type: dot_pearson
2235
+ value: 31.125303796647056
2236
+ - type: dot_spearman
2237
+ value: 32.31662126895294
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: 0.219
2249
+ - type: map_at_10
2250
+ value: 1.7469999999999999
2251
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+ value: 10.177999999999999
2253
+ - type: map_at_1000
2254
+ value: 26.108999999999998
2255
+ - type: map_at_3
2256
+ value: 0.64
2257
+ - type: map_at_5
2258
+ value: 0.968
2259
+ - type: mrr_at_1
2260
+ value: 82
2261
+ - type: mrr_at_10
2262
+ value: 89.067
2263
+ - type: mrr_at_100
2264
+ value: 89.067
2265
+ - type: mrr_at_1000
2266
+ value: 89.067
2267
+ - type: mrr_at_3
2268
+ value: 88.333
2269
+ - type: mrr_at_5
2270
+ value: 88.73299999999999
2271
+ - type: ndcg_at_1
2272
+ value: 78
2273
+ - type: ndcg_at_10
2274
+ value: 71.398
2275
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+ value: 55.574999999999996
2277
+ - type: ndcg_at_1000
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+ value: 51.771
2279
+ - type: ndcg_at_3
2280
+ value: 77.765
2281
+ - type: ndcg_at_5
2282
+ value: 73.614
2283
+ - type: precision_at_1
2284
+ value: 82
2285
+ - type: precision_at_10
2286
+ value: 75.4
2287
+ - type: precision_at_100
2288
+ value: 58.040000000000006
2289
+ - type: precision_at_1000
2290
+ value: 23.516000000000002
2291
+ - type: precision_at_3
2292
+ value: 84
2293
+ - type: precision_at_5
2294
+ value: 78.4
2295
+ - type: recall_at_1
2296
+ value: 0.219
2297
+ - type: recall_at_10
2298
+ value: 1.958
2299
+ - type: recall_at_100
2300
+ value: 13.797999999999998
2301
+ - type: recall_at_1000
2302
+ value: 49.881
2303
+ - type: recall_at_3
2304
+ value: 0.672
2305
+ - type: recall_at_5
2306
+ value: 1.0370000000000001
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: 1.8610000000000002
2318
+ - type: map_at_10
2319
+ value: 8.705
2320
+ - type: map_at_100
2321
+ value: 15.164
2322
+ - type: map_at_1000
2323
+ value: 16.78
2324
+ - type: map_at_3
2325
+ value: 4.346
2326
+ - type: map_at_5
2327
+ value: 6.151
2328
+ - type: mrr_at_1
2329
+ value: 22.448999999999998
2330
+ - type: mrr_at_10
2331
+ value: 41.556
2332
+ - type: mrr_at_100
2333
+ value: 42.484
2334
+ - type: mrr_at_1000
2335
+ value: 42.494
2336
+ - type: mrr_at_3
2337
+ value: 37.755
2338
+ - type: mrr_at_5
2339
+ value: 40.102
2340
+ - type: ndcg_at_1
2341
+ value: 21.429000000000002
2342
+ - type: ndcg_at_10
2343
+ value: 23.439
2344
+ - type: ndcg_at_100
2345
+ value: 36.948
2346
+ - type: ndcg_at_1000
2347
+ value: 48.408
2348
+ - type: ndcg_at_3
2349
+ value: 22.261
2350
+ - type: ndcg_at_5
2351
+ value: 23.085
2352
+ - type: precision_at_1
2353
+ value: 22.448999999999998
2354
+ - type: precision_at_10
2355
+ value: 21.633
2356
+ - type: precision_at_100
2357
+ value: 8.02
2358
+ - type: precision_at_1000
2359
+ value: 1.5939999999999999
2360
+ - type: precision_at_3
2361
+ value: 23.810000000000002
2362
+ - type: precision_at_5
2363
+ value: 24.490000000000002
2364
+ - type: recall_at_1
2365
+ value: 1.8610000000000002
2366
+ - type: recall_at_10
2367
+ value: 15.876000000000001
2368
+ - type: recall_at_100
2369
+ value: 50.300999999999995
2370
+ - type: recall_at_1000
2371
+ value: 86.098
2372
+ - type: recall_at_3
2373
+ value: 5.892
2374
+ - type: recall_at_5
2375
+ value: 9.443
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: 70.3264
2387
+ - type: ap
2388
+ value: 13.249577616243794
2389
+ - type: f1
2390
+ value: 53.621518367695685
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: 61.57611771363894
2402
+ - type: f1
2403
+ value: 61.79797478568639
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: 53.38315344479284
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: 87.55438993860642
2426
+ - type: cos_sim_ap
2427
+ value: 77.98702600017738
2428
+ - type: cos_sim_f1
2429
+ value: 71.94971653931476
2430
+ - type: cos_sim_precision
2431
+ value: 67.50693802035153
2432
+ - type: cos_sim_recall
2433
+ value: 77.01846965699208
2434
+ - type: dot_accuracy
2435
+ value: 87.55438993860642
2436
+ - type: dot_ap
2437
+ value: 77.98702925907986
2438
+ - type: dot_f1
2439
+ value: 71.94971653931476
2440
+ - type: dot_precision
2441
+ value: 67.50693802035153
2442
+ - type: dot_recall
2443
+ value: 77.01846965699208
2444
+ - type: euclidean_accuracy
2445
+ value: 87.55438993860642
2446
+ - type: euclidean_ap
2447
+ value: 77.98702951957925
2448
+ - type: euclidean_f1
2449
+ value: 71.94971653931476
2450
+ - type: euclidean_precision
2451
+ value: 67.50693802035153
2452
+ - type: euclidean_recall
2453
+ value: 77.01846965699208
2454
+ - type: manhattan_accuracy
2455
+ value: 87.54246885617214
2456
+ - type: manhattan_ap
2457
+ value: 77.95531413902947
2458
+ - type: manhattan_f1
2459
+ value: 71.93605683836589
2460
+ - type: manhattan_precision
2461
+ value: 69.28152492668622
2462
+ - type: manhattan_recall
2463
+ value: 74.80211081794195
2464
+ - type: max_accuracy
2465
+ value: 87.55438993860642
2466
+ - type: max_ap
2467
+ value: 77.98702951957925
2468
+ - type: max_f1
2469
+ value: 71.94971653931476
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: 89.47296930182016
2481
+ - type: cos_sim_ap
2482
+ value: 86.92853616302108
2483
+ - type: cos_sim_f1
2484
+ value: 79.35138351681047
2485
+ - type: cos_sim_precision
2486
+ value: 76.74820143884892
2487
+ - type: cos_sim_recall
2488
+ value: 82.13735756082538
2489
+ - type: dot_accuracy
2490
+ value: 89.47296930182016
2491
+ - type: dot_ap
2492
+ value: 86.92854339601595
2493
+ - type: dot_f1
2494
+ value: 79.35138351681047
2495
+ - type: dot_precision
2496
+ value: 76.74820143884892
2497
+ - type: dot_recall
2498
+ value: 82.13735756082538
2499
+ - type: euclidean_accuracy
2500
+ value: 89.47296930182016
2501
+ - type: euclidean_ap
2502
+ value: 86.92854191061649
2503
+ - type: euclidean_f1
2504
+ value: 79.35138351681047
2505
+ - type: euclidean_precision
2506
+ value: 76.74820143884892
2507
+ - type: euclidean_recall
2508
+ value: 82.13735756082538
2509
+ - type: manhattan_accuracy
2510
+ value: 89.47685023479644
2511
+ - type: manhattan_ap
2512
+ value: 86.90063722679578
2513
+ - type: manhattan_f1
2514
+ value: 79.30753865502702
2515
+ - type: manhattan_precision
2516
+ value: 76.32066068631639
2517
+ - type: manhattan_recall
2518
+ value: 82.53772713273791
2519
+ - type: max_accuracy
2520
+ value: 89.47685023479644
2521
+ - type: max_ap
2522
+ value: 86.92854339601595
2523
+ - type: max_f1
2524
+ value: 79.35138351681047
2525
+ duplicated_from: hkunlp/instructor-xl
2526
+ ---
2527
+
2528
+ # hkunlp/instructor-xl
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!
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
+ * 01/21: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-xl) 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-xl) 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-xl')
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
+ {
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+ "_name_or_path": "/home2/huggingface/outputs/xl_30000_fever/checkpoint-300/",
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+ "architectures": [
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+ "T5EncoderModel"
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+ ],
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+ "d_ff": 16384,
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+ "feed_forward_proj": "relu",
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+ "initializer_factor": 1.0,
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+ "is_encoder_decoder": true,
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+ "is_gated_act": false,
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+ "layer_norm_epsilon": 1e-06,
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+ "model_type": "t5",
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+ "n_positions": 512,
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+ "num_decoder_layers": 24,
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+ "num_heads": 32,
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+ "num_layers": 24,
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+ "output_past": true,
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+ "pad_token_id": 0,
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