philmas/cese5020-contrastive-model

SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5

This is a sentence-transformers model finetuned from Alibaba-NLP/gte-base-en-v1.5. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: Alibaba-NLP/gte-base-en-v1.5
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NewModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("philmas/cese5020-contrastive-model")
# Run inference
sentences = [
    'Aluminium alloy wire, 10mm diameter, used in electrical transmission.',
    'Sunnah Dates - High-quality, nutrient-rich dates ideal for religious celebrations and dietary needs.',
    'Cocoa mass, suitable for coating confectioneries.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Triplet

• Dataset: eval
• Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.8725

Training Details

Training Dataset

Unnamed Dataset

• Size: 170,845 training samples
• Columns: text and label
• Approximate statistics based on the first 1000 samples:

  	text	label
  type	string	int
  details	min: 5 tokens mean: 15.77 tokens max: 34 tokens	6: ~0.20% 9: ~0.10% 17: ~0.10% 20: ~0.10% 22: ~0.10% 25: ~0.10% 28: ~0.10% 32: ~0.10% 53: ~0.10% 73: ~0.10% 81: ~0.10% 86: ~0.10% 91: ~0.10% 98: ~0.10% 100: ~0.10% 108: ~0.20% 116: ~0.10% 118: ~0.10% 119: ~0.10% 120: ~0.10% 122: ~0.10% 127: ~0.10% 131: ~0.10% 132: ~0.10% 134: ~0.10% 138: ~0.10% 139: ~0.10% 140: ~0.10% 146: ~0.10% 168: ~0.10% 170: ~0.10% 175: ~0.10% 179: ~0.20% 186: ~0.10% 187: ~0.10% 192: ~0.10% 193: ~0.10% 194: ~0.20% 199: ~0.10% 203: ~0.10% 213: ~0.10% 221: ~0.10% 235: ~0.20% 246: ~0.10% 247: ~0.20% 255: ~0.20% 258: ~0.10% 263: ~0.20% 266: ~0.10% 267: ~0.10% 274: ~0.10% 277: ~0.10% 288: ~0.10% 290: ~0.10% 298: ~0.20% 300: ~0.10% 304: ~0.10% 306: ~0.10% 308: ~0.10% 311: ~0.10% 317: ~0.10% 321: ~0.10% 331: ~0.10% 340: ~0.10% 345: ~0.10% 346: ~0.10% 347: ~0.10% 361: ~0.10% 370: ~0.10% 373: ~0.10% 390: ~0.30% 403: ~0.10% 409: ~0.10% 411: ~0.10% 414: ~0.10% 432: ~0.10% 433: ~0.20% 435: ~0.10% 447: ~0.10% 449: ~0.10% 451: ~0.10% 460: ~0.10% 464: ~0.10% 478: ~0.10% 480: ~0.10% 482: ~0.10% 484: ~0.10% 496: ~0.10% 506: ~0.10% 509: ~0.10% 513: ~0.10% 520: ~0.10% 527: ~0.10% 531: ~0.10% 535: ~0.10% 541: ~0.10% 546: ~0.10% 552: ~0.10% 556: ~0.20% 576: ~0.10% 580: ~0.10% 581: ~0.10% 583: ~0.10% 584: ~0.10% 587: ~0.10% 596: ~0.10% 601: ~0.10% 602: ~0.10% 603: ~0.20% 605: ~0.10% 608: ~0.10% 612: ~0.10% 620: ~0.10% 625: ~0.10% 628: ~0.10% 641: ~0.10% 645: ~0.10% 650: ~0.10% 656: ~0.10% 659: ~0.10% 660: ~0.20% 661: ~0.10% 666: ~0.20% 668: ~0.10% 670: ~0.10% 688: ~0.10% 690: ~0.10% 696: ~0.20% 711: ~0.10% 713: ~0.10% 714: ~0.10% 726: ~0.20% 727: ~0.10% 735: ~0.10% 736: ~0.10% 737: ~0.10% 740: ~0.10% 741: ~0.10% 744: ~0.10% 746: ~0.10% 754: ~0.10% 756: ~0.20% 757: ~0.20% 762: ~0.10% 763: ~0.10% 768: ~0.10% 776: ~0.10% 781: ~0.10% 787: ~0.10% 789: ~0.10% 791: ~0.10% 806: ~0.10% 809: ~0.10% 814: ~0.10% 820: ~0.10% 824: ~0.20% 833: ~0.10% 837: ~0.10% 857: ~0.10% 858: ~0.20% 860: ~0.10% 879: ~0.10% 882: ~0.10% 893: ~0.10% 895: ~0.10% 899: ~0.10% 919: ~0.10% 925: ~0.10% 926: ~0.10% 945: ~0.10% 957: ~0.10% 959: ~0.10% 973: ~0.10% 978: ~0.10% 981: ~0.20% 982: ~0.10% 991: ~0.10% 996: ~0.10% 1009: ~0.10% 1012: ~0.10% 1014: ~0.10% 1015: ~0.10% 1017: ~0.10% 1020: ~0.10% 1024: ~0.10% 1030: ~0.10% 1034: ~0.10% 1036: ~0.10% 1038: ~0.10% 1039: ~0.10% 1047: ~0.10% 1049: ~0.10% 1051: ~0.10% 1054: ~0.10% 1063: ~0.10% 1069: ~0.10% 1070: ~0.10% 1072: ~0.10% 1074: ~0.10% 1081: ~0.10% 1097: ~0.10% 1102: ~0.10% 1109: ~0.10% 1113: ~0.10% 1135: ~0.10% 1139: ~0.10% 1155: ~0.10% 1161: ~0.10% 1176: ~0.10% 1180: ~0.20% 1181: ~0.10% 1188: ~0.10% 1198: ~0.10% 1200: ~0.10% 1215: ~0.10% 1224: ~0.10% 1227: ~0.10% 1229: ~0.10% 1235: ~0.10% 1249: ~0.10% 1265: ~0.10% 1268: ~0.10% 1275: ~0.20% 1282: ~0.10% 1286: ~0.10% 1289: ~0.10% 1290: ~0.10% 1296: ~0.10% 1309: ~0.10% 1318: ~0.10% 1322: ~0.10% 1323: ~0.10% 1327: ~0.10% 1338: ~0.10% 1342: ~0.10% 1345: ~0.10% 1347: ~0.10% 1348: ~0.10% 1350: ~0.10% 1352: ~0.10% 1354: ~0.10% 1362: ~0.10% 1367: ~0.10% 1371: ~0.10% 1375: ~0.10% 1377: ~0.10% 1392: ~0.10% 1397: ~0.10% 1400: ~0.10% 1401: ~0.10% 1408: ~0.10% 1411: ~0.10% 1419: ~0.10% 1424: ~0.10% 1429: ~0.10% 1445: ~0.10% 1446: ~0.10% 1450: ~0.10% 1454: ~0.10% 1455: ~0.10% 1462: ~0.10% 1467: ~0.10% 1468: ~0.10% 1469: ~0.10% 1471: ~0.10% 1514: ~0.10% 1518: ~0.10% 1522: ~0.10% 1524: ~0.10% 1529: ~0.10% 1531: ~0.10% 1534: ~0.10% 1553: ~0.10% 1560: ~0.10% 1563: ~0.10% 1564: ~0.10% 1566: ~0.10% 1574: ~0.10% 1578: ~0.10% 1580: ~0.10% 1581: ~0.20% 1587: ~0.10% 1598: ~0.20% 1609: ~0.10% 1610: ~0.10% 1616: ~0.10% 1632: ~0.10% 1634: ~0.10% 1645: ~0.10% 1646: ~0.10% 1650: ~0.10% 1654: ~0.10% 1660: ~0.10% 1661: ~0.20% 1662: ~0.10% 1664: ~0.10% 1671: ~0.10% 1680: ~0.10% 1686: ~0.10% 1688: ~0.10% 1693: ~0.10% 1698: ~0.10% 1702: ~0.10% 1710: ~0.10% 1726: ~0.10% 1727: ~0.10% 1730: ~0.10% 1731: ~0.10% 1741: ~0.10% 1743: ~0.10% 1744: ~0.10% 1749: ~0.10% 1755: ~0.10% 1760: ~0.10% 1766: ~0.10% 1769: ~0.10% 1785: ~0.10% 1789: ~0.10% 1790: ~0.10% 1791: ~0.10% 1798: ~0.10% 1800: ~0.10% 1804: ~0.10% 1805: ~0.10% 1811: ~0.10% 1814: ~0.10% 1828: ~0.10% 1836: ~0.10% 1838: ~0.10% 1842: ~0.10% 1847: ~0.10% 1860: ~0.10% 1861: ~0.10% 1863: ~0.10% 1864: ~0.10% 1869: ~0.10% 1879: ~0.10% 1882: ~0.10% 1886: ~0.10% 1891: ~0.10% 1897: ~0.10% 1910: ~0.10% 1913: ~0.20% 1928: ~0.10% 1932: ~0.20% 1933: ~0.10% 1943: ~0.10% 1944: ~0.10% 1960: ~0.10% 1965: ~0.10% 1968: ~0.10% 1971: ~0.10% 1979: ~0.10% 1981: ~0.10% 1984: ~0.10% 1985: ~0.20% 1986: ~0.10% 1991: ~0.10% 1992: ~0.10% 1998: ~0.10% 1999: ~0.10% 2000: ~0.10% 2004: ~0.10% 2006: ~0.20% 2019: ~0.10% 2026: ~0.20% 2050: ~0.10% 2073: ~0.10% 2082: ~0.10% 2093: ~0.10% 2117: ~0.10% 2119: ~0.10% 2128: ~0.10% 2130: ~0.10% 2136: ~0.10% 2141: ~0.10% 2143: ~0.10% 2149: ~0.10% 2151: ~0.10% 2153: ~0.10% 2155: ~0.10% 2161: ~0.20% 2171: ~0.10% 2178: ~0.10% 2180: ~0.10% 2191: ~0.10% 2201: ~0.10% 2206: ~0.10% 2207: ~0.10% 2210: ~0.10% 2213: ~0.10% 2226: ~0.10% 2238: ~0.10% 2242: ~0.10% 2250: ~0.20% 2257: ~0.10% 2263: ~0.10% 2264: ~0.20% 2272: ~0.10% 2273: ~0.10% 2282: ~0.10% 2287: ~0.10% 2288: ~0.10% 2292: ~0.10% 2308: ~0.20% 2331: ~0.10% 2339: ~0.10% 2341: ~0.20% 2342: ~0.10% 2354: ~0.20% 2357: ~0.10% 2358: ~0.10% 2363: ~0.10% 2368: ~0.10% 2372: ~0.10% 2379: ~0.10% 2381: ~0.10% 2384: ~0.10% 2386: ~0.10% 2396: ~0.10% 2399: ~0.10% 2402: ~0.10% 2403: ~0.10% 2406: ~0.10% 2411: ~0.10% 2413: ~0.10% 2427: ~0.10% 2434: ~0.10% 2437: ~0.10% 2446: ~0.10% 2451: ~0.10% 2463: ~0.10% 2466: ~0.10% 2468: ~0.10% 2470: ~0.10% 2477: ~0.10% 2479: ~0.10% 2480: ~0.20% 2489: ~0.10% 2504: ~0.10% 2505: ~0.10% 2511: ~0.10% 2515: ~0.10% 2519: ~0.10% 2527: ~0.10% 2528: ~0.10% 2530: ~0.10% 2544: ~0.10% 2551: ~0.10% 2553: ~0.10% 2557: ~0.10% 2562: ~0.10% 2564: ~0.10% 2581: ~0.10% 2587: ~0.10% 2589: ~0.10% 2596: ~0.10% 2599: ~0.10% 2600: ~0.10% 2608: ~0.10% 2609: ~0.10% 2614: ~0.20% 2623: ~0.10% 2636: ~0.10% 2638: ~0.10% 2644: ~0.10% 2658: ~0.10% 2659: ~0.10% 2662: ~0.10% 2663: ~0.10% 2673: ~0.10% 2674: ~0.10% 2675: ~0.10% 2679: ~0.10% 2680: ~0.10% 2686: ~0.10% 2699: ~0.10% 2707: ~0.10% 2713: ~0.10% 2716: ~0.10% 2723: ~0.20% 2726: ~0.10% 2729: ~0.10% 2748: ~0.10% 2759: ~0.10% 2760: ~0.10% 2762: ~0.10% 2770: ~0.10% 2771: ~0.10% 2772: ~0.10% 2798: ~0.10% 2799: ~0.10% 2800: ~0.20% 2803: ~0.10% 2816: ~0.10% 2822: ~0.20% 2826: ~0.10% 2829: ~0.10% 2836: ~0.10% 2842: ~0.10% 2844: ~0.10% 2848: ~0.10% 2853: ~0.10% 2854: ~0.10% 2861: ~0.10% 2862: ~0.10% 2890: ~0.10% 2895: ~0.10% 2901: ~0.10% 2904: ~0.10% 2906: ~0.10% 2907: ~0.10% 2908: ~0.10% 2912: ~0.10% 2917: ~0.10% 2922: ~0.10% 2930: ~0.10% 2931: ~0.10% 2932: ~0.10% 2934: ~0.10% 2942: ~0.10% 2943: ~0.10% 2954: ~0.20% 2958: ~0.10% 2963: ~0.10% 2965: ~0.10% 2968: ~0.10% 2969: ~0.10% 2970: ~0.20% 2973: ~0.10% 2977: ~0.10% 2993: ~0.10% 2999: ~0.10% 3005: ~0.10% 3009: ~0.10% 3018: ~0.10% 3019: ~0.10% 3029: ~0.10% 3030: ~0.10% 3038: ~0.10% 3039: ~0.10% 3040: ~0.10% 3052: ~0.10% 3057: ~0.10% 3059: ~0.10% 3060: ~0.20% 3062: ~0.10% 3068: ~0.10% 3072: ~0.10% 3076: ~0.10% 3092: ~0.10% 3100: ~0.10% 3105: ~0.10% 3108: ~0.10% 3120: ~0.10% 3121: ~0.10% 3122: ~0.10% 3124: ~0.10% 3128: ~0.10% 3132: ~0.10% 3138: ~0.10% 3142: ~0.10% 3143: ~0.10% 3157: ~0.10% 3158: ~0.10% 3159: ~0.10% 3160: ~0.10% 3184: ~0.10% 3186: ~0.10% 3194: ~0.10% 3202: ~0.10% 3209: ~0.10% 3213: ~0.10% 3214: ~0.10% 3216: ~0.10% 3219: ~0.10% 3227: ~0.10% 3230: ~0.10% 3231: ~0.10% 3233: ~0.10% 3249: ~0.10% 3250: ~0.10% 3257: ~0.10% 3268: ~0.10% 3269: ~0.10% 3273: ~0.10% 3274: ~0.10% 3281: ~0.10% 3292: ~0.10% 3295: ~0.10% 3297: ~0.10% 3300: ~0.10% 3309: ~0.10% 3315: ~0.10% 3316: ~0.10% 3321: ~0.10% 3339: ~0.30% 3360: ~0.10% 3364: ~0.10% 3365: ~0.10% 3369: ~0.10% 3373: ~0.10% 3375: ~0.10% 3383: ~0.20% 3386: ~0.10% 3388: ~0.10% 3389: ~0.10% 3394: ~0.10% 3398: ~0.10% 3400: ~0.10% 3433: ~0.10% 3439: ~0.10% 3457: ~0.10% 3463: ~0.20% 3464: ~0.10% 3465: ~0.10% 3466: ~0.10% 3478: ~0.10% 3518: ~0.10% 3520: ~0.10% 3524: ~0.10% 3525: ~0.10% 3526: ~0.10% 3533: ~0.10% 3534: ~0.10% 3540: ~0.10% 3569: ~0.10% 3572: ~0.10% 3587: ~0.10% 3592: ~0.20% 3607: ~0.10% 3608: ~0.10% 3613: ~0.10% 3615: ~0.10% 3620: ~0.20% 3621: ~0.10% 3626: ~0.10% 3635: ~0.20% 3637: ~0.10% 3640: ~0.10% 3642: ~0.10% 3644: ~0.10% 3647: ~0.10% 3659: ~0.10% 3666: ~0.10% 3669: ~0.10% 3671: ~0.10% 3685: ~0.10% 3707: ~0.10% 3712: ~0.10% 3722: ~0.10% 3724: ~0.10% 3727: ~0.10% 3728: ~0.10% 3733: ~0.10% 3734: ~0.10% 3741: ~0.10% 3752: ~0.10% 3762: ~0.20% 3766: ~0.10% 3774: ~0.10% 3779: ~0.10% 3781: ~0.10% 3783: ~0.10% 3786: ~0.10% 3793: ~0.10% 3798: ~0.10% 3803: ~0.10% 3804: ~0.10% 3820: ~0.10% 3823: ~0.10% 3828: ~0.10% 3838: ~0.10% 3841: ~0.10% 3851: ~0.10% 3853: ~0.10% 3857: ~0.10% 3866: ~0.20% 3869: ~0.10% 3873: ~0.10% 3874: ~0.10% 3881: ~0.10% 3894: ~0.10% 3901: ~0.10% 3902: ~0.10% 3908: ~0.10% 3912: ~0.10% 3913: ~0.10% 3921: ~0.10% 3922: ~0.10% 3932: ~0.10% 3935: ~0.10% 3949: ~0.10% 3952: ~0.10% 3956: ~0.10% 3963: ~0.20% 3965: ~0.10% 3967: ~0.10% 3984: ~0.10% 4000: ~0.10% 4014: ~0.10% 4015: ~0.10% 4018: ~0.10% 4019: ~0.10% 4023: ~0.10% 4026: ~0.10% 4033: ~0.10% 4034: ~0.10% 4040: ~0.10% 4041: ~0.20% 4072: ~0.10% 4074: ~0.10% 4084: ~0.10% 4086: ~0.10% 4088: ~0.10% 4091: ~0.10% 4092: ~0.10% 4095: ~0.10% 4099: ~0.10% 4108: ~0.10% 4133: ~0.10% 4137: ~0.10% 4145: ~0.20% 4147: ~0.10% 4150: ~0.10% 4151: ~0.10% 4164: ~0.20% 4165: ~0.10% 4166: ~0.10% 4173: ~0.10% 4181: ~0.10% 4193: ~0.10% 4201: ~0.10% 4207: ~0.10% 4208: ~0.10% 4209: ~0.10% 4215: ~0.10% 4233: ~0.20% 4235: ~0.10% 4241: ~0.10% 4242: ~0.10% 4247: ~0.10% 4250: ~0.10% 4259: ~0.10% 4271: ~0.10% 4282: ~0.10% 4290: ~0.10% 4299: ~0.10% 4305: ~0.10% 4306: ~0.10% 4314: ~0.20% 4331: ~0.10% 4341: ~0.10% 4342: ~0.10% 4344: ~0.10% 4348: ~0.10% 4351: ~0.10% 4359: ~0.10% 4379: ~0.10% 4393: ~0.10% 4400: ~0.10% 4411: ~0.10% 4416: ~0.10% 4419: ~0.10% 4425: ~0.10% 4426: ~0.10% 4428: ~0.10% 4431: ~0.10% 4438: ~0.20% 4441: ~0.20% 4442: ~0.10% 4448: ~0.20% 4453: ~0.10% 4455: ~0.10% 4470: ~0.10% 4471: ~0.10% 4482: ~0.10% 4483: ~0.10% 4489: ~0.20% 4490: ~0.10% 4493: ~0.10% 4498: ~0.10% 4501: ~0.10% 4503: ~0.20% 4504: ~0.10% 4523: ~0.20% 4529: ~0.10% 4546: ~0.20% 4549: ~0.10% 4564: ~0.10% 4569: ~0.10% 4572: ~0.10% 4573: ~0.10% 4574: ~0.10% 4575: ~0.10% 4576: ~0.10% 4581: ~0.10% 4582: ~0.10% 4591: ~0.10% 4596: ~0.10% 4602: ~0.10% 4604: ~0.10% 4612: ~0.10% 4613: ~0.20% 4615: ~0.10% 4616: ~0.10% 4617: ~0.10% 4622: ~0.10% 4623: ~0.10% 4625: ~0.10% 4626: ~0.10% 4628: ~0.10% 4633: ~0.10% 4635: ~0.20% 4650: ~0.10% 4651: ~0.10% 4660: ~0.10% 4668: ~0.10% 4680: ~0.10% 4681: ~0.10% 4684: ~0.10% 4686: ~0.10% 4689: ~0.10% 4702: ~0.10% 4703: ~0.10% 4707: ~0.10% 4718: ~0.10% 4719: ~0.10% 4724: ~0.10% 4744: ~0.10% 4753: ~0.10% 4758: ~0.10% 4762: ~0.10% 4766: ~0.10% 4767: ~0.10% 4768: ~0.10% 4778: ~0.10% 4784: ~0.10% 4791: ~0.10% 4792: ~0.10% 4795: ~0.10% 4809: ~0.10% 4814: ~0.10% 4817: ~0.10% 4823: ~0.10% 4828: ~0.10% 4834: ~0.10% 4846: ~0.10% 4849: ~0.10% 4856: ~0.10% 4862: ~0.10% 4864: ~0.10% 4871: ~0.10% 4876: ~0.10% 4880: ~0.10% 4888: ~0.20% 4894: ~0.10% 4899: ~0.10% 4908: ~0.10% 4912: ~0.10% 4917: ~0.10% 4918: ~0.10% 4926: ~0.10% 4931: ~0.10% 4935: ~0.10% 4948: ~0.10% 4960: ~0.10% 4964: ~0.10% 4965: ~0.10% 4968: ~0.10% 4970: ~0.10% 4978: ~0.20% 4982: ~0.10% 4991: ~0.10% 4997: ~0.10% 5004: ~0.10% 5010: ~0.10% 5034: ~0.10% 5038: ~0.10% 5046: ~0.10% 5049: ~0.10% 5052: ~0.10% 5061: ~0.10% 5064: ~0.10% 5077: ~0.10% 5099: ~0.10% 5109: ~0.10% 5114: ~0.10% 5115: ~0.10% 5122: ~0.10% 5126: ~0.10% 5131: ~0.20% 5147: ~0.10% 5152: ~0.10% 5156: ~0.10% 5168: ~0.10% 5187: ~0.10% 5195: ~0.10% 5200: ~0.10% 5202: ~0.30% 5208: ~0.10% 5217: ~0.10% 5220: ~0.10% 5225: ~0.10% 5226: ~0.10% 5229: ~0.10% 5233: ~0.10% 5248: ~0.10% 5257: ~0.10% 5258: ~0.10% 5273: ~0.10% 5277: ~0.10% 5284: ~0.10% 5285: ~0.10% 5289: ~0.10% 5293: ~0.10% 5300: ~0.10% 5310: ~0.20% 5312: ~0.10% 5320: ~0.10% 5321: ~0.10% 5326: ~0.10% 5338: ~0.10%

• Samples:

  text	label
  Continuous Flow Liquid Analyzer	5077
  Spicy chili powder mix with salt and garlic.	535
  Brined and dried fillets of fine fish, suitable for a variety of dishes.	246

• Loss: BatchHardTripletLoss

Evaluation Dataset

Unnamed Dataset

• Size: 10,682 evaluation samples
• Columns: text and label
• Approximate statistics based on the first 1000 samples:

  	text	label
  type	string	int
  details	min: 4 tokens mean: 15.63 tokens max: 37 tokens	4: ~0.10% 21: ~0.10% 38: ~0.10% 41: ~0.10% 42: ~0.10% 45: ~0.10% 64: ~0.10% 69: ~0.10% 83: ~0.10% 87: ~0.10% 90: ~0.10% 101: ~0.10% 105: ~0.10% 117: ~0.20% 120: ~0.10% 121: ~0.10% 123: ~0.10% 126: ~0.10% 127: ~0.10% 128: ~0.10% 141: ~0.10% 147: ~0.10% 162: ~0.10% 163: ~0.10% 171: ~0.10% 179: ~0.10% 194: ~0.10% 203: ~0.10% 204: ~0.10% 205: ~0.10% 207: ~0.10% 208: ~0.10% 209: ~0.10% 215: ~0.10% 219: ~0.10% 222: ~0.10% 226: ~0.10% 236: ~0.10% 243: ~0.10% 256: ~0.10% 263: ~0.10% 280: ~0.10% 281: ~0.10% 286: ~0.10% 291: ~0.10% 293: ~0.10% 295: ~0.10% 297: ~0.10% 302: ~0.10% 303: ~0.10% 304: ~0.10% 308: ~0.10% 311: ~0.10% 320: ~0.10% 322: ~0.10% 328: ~0.10% 342: ~0.10% 343: ~0.10% 349: ~0.10% 355: ~0.10% 361: ~0.20% 363: ~0.10% 364: ~0.10% 365: ~0.10% 370: ~0.10% 371: ~0.10% 383: ~0.10% 385: ~0.10% 386: ~0.10% 398: ~0.10% 406: ~0.10% 427: ~0.10% 430: ~0.10% 434: ~0.10% 450: ~0.10% 451: ~0.10% 453: ~0.10% 457: ~0.10% 462: ~0.10% 467: ~0.10% 476: ~0.10% 479: ~0.10% 480: ~0.10% 484: ~0.10% 490: ~0.10% 492: ~0.10% 497: ~0.10% 510: ~0.10% 512: ~0.10% 516: ~0.20% 518: ~0.10% 519: ~0.10% 525: ~0.10% 528: ~0.10% 531: ~0.10% 534: ~0.10% 552: ~0.10% 555: ~0.10% 558: ~0.10% 563: ~0.10% 566: ~0.10% 567: ~0.10% 574: ~0.10% 582: ~0.10% 587: ~0.10% 598: ~0.10% 604: ~0.10% 606: ~0.10% 614: ~0.10% 621: ~0.10% 622: ~0.10% 630: ~0.10% 640: ~0.10% 645: ~0.10% 668: ~0.10% 670: ~0.10% 677: ~0.10% 682: ~0.10% 683: ~0.10% 688: ~0.10% 720: ~0.10% 734: ~0.10% 744: ~0.10% 753: ~0.10% 756: ~0.10% 759: ~0.10% 763: ~0.10% 767: ~0.10% 770: ~0.10% 773: ~0.20% 775: ~0.10% 778: ~0.10% 779: ~0.10% 783: ~0.10% 784: ~0.10% 785: ~0.10% 787: ~0.10% 788: ~0.10% 789: ~0.10% 801: ~0.10% 802: ~0.10% 811: ~0.10% 812: ~0.10% 827: ~0.10% 828: ~0.10% 836: ~0.10% 843: ~0.10% 844: ~0.10% 846: ~0.10% 848: ~0.10% 851: ~0.10% 872: ~0.20% 875: ~0.10% 878: ~0.10% 879: ~0.10% 881: ~0.10% 890: ~0.10% 894: ~0.10% 895: ~0.10% 897: ~0.10% 899: ~0.10% 900: ~0.10% 903: ~0.10% 927: ~0.10% 929: ~0.10% 940: ~0.10% 945: ~0.10% 953: ~0.10% 957: ~0.10% 963: ~0.10% 968: ~0.10% 969: ~0.10% 975: ~0.10% 976: ~0.10% 983: ~0.10% 991: ~0.10% 993: ~0.20% 994: ~0.10% 997: ~0.10% 1001: ~0.10% 1036: ~0.10% 1042: ~0.10% 1074: ~0.10% 1078: ~0.10% 1080: ~0.10% 1085: ~0.10% 1092: ~0.10% 1097: ~0.10% 1104: ~0.10% 1107: ~0.10% 1119: ~0.10% 1124: ~0.10% 1139: ~0.10% 1142: ~0.10% 1181: ~0.10% 1186: ~0.10% 1201: ~0.10% 1206: ~0.10% 1211: ~0.10% 1213: ~0.10% 1225: ~0.10% 1232: ~0.10% 1236: ~0.10% 1237: ~0.10% 1238: ~0.10% 1240: ~0.10% 1256: ~0.10% 1259: ~0.10% 1266: ~0.10% 1273: ~0.10% 1277: ~0.10% 1279: ~0.10% 1280: ~0.10% 1281: ~0.10% 1284: ~0.10% 1286: ~0.10% 1292: ~0.10% 1308: ~0.10% 1321: ~0.10% 1340: ~0.10% 1346: ~0.10% 1348: ~0.10% 1355: ~0.10% 1369: ~0.20% 1370: ~0.10% 1371: ~0.20% 1390: ~0.10% 1391: ~0.10% 1394: ~0.10% 1396: ~0.10% 1403: ~0.10% 1409: ~0.10% 1410: ~0.10% 1412: ~0.10% 1414: ~0.20% 1416: ~0.10% 1420: ~0.10% 1423: ~0.10% 1427: ~0.10% 1430: ~0.10% 1431: ~0.10% 1432: ~0.10% 1437: ~0.10% 1462: ~0.10% 1467: ~0.10% 1472: ~0.10% 1474: ~0.10% 1475: ~0.10% 1479: ~0.10% 1494: ~0.10% 1497: ~0.10% 1503: ~0.10% 1505: ~0.10% 1506: ~0.10% 1507: ~0.10% 1508: ~0.10% 1518: ~0.10% 1523: ~0.10% 1529: ~0.10% 1533: ~0.10% 1543: ~0.10% 1552: ~0.10% 1558: ~0.10% 1569: ~0.10% 1576: ~0.10% 1577: ~0.10% 1578: ~0.10% 1583: ~0.10% 1587: ~0.10% 1589: ~0.10% 1590: ~0.10% 1598: ~0.10% 1611: ~0.10% 1613: ~0.10% 1614: ~0.10% 1621: ~0.10% 1629: ~0.10% 1630: ~0.10% 1631: ~0.10% 1633: ~0.10% 1634: ~0.10% 1646: ~0.10% 1647: ~0.10% 1653: ~0.10% 1670: ~0.10% 1675: ~0.10% 1685: ~0.10% 1697: ~0.10% 1708: ~0.10% 1731: ~0.10% 1740: ~0.10% 1743: ~0.10% 1750: ~0.10% 1752: ~0.10% 1755: ~0.10% 1762: ~0.10% 1768: ~0.10% 1773: ~0.10% 1792: ~0.10% 1795: ~0.10% 1798: ~0.10% 1801: ~0.10% 1807: ~0.10% 1817: ~0.10% 1824: ~0.10% 1825: ~0.20% 1828: ~0.10% 1833: ~0.10% 1852: ~0.10% 1854: ~0.10% 1855: ~0.10% 1856: ~0.10% 1859: ~0.10% 1867: ~0.10% 1869: ~0.10% 1874: ~0.10% 1886: ~0.10% 1889: ~0.10% 1892: ~0.10% 1893: ~0.10% 1894: ~0.10% 1901: ~0.10% 1910: ~0.10% 1911: ~0.10% 1921: ~0.10% 1922: ~0.10% 1925: ~0.10% 1933: ~0.20% 1934: ~0.10% 1937: ~0.10% 1940: ~0.20% 1941: ~0.10% 1954: ~0.10% 1969: ~0.10% 1974: ~0.20% 1986: ~0.10% 1988: ~0.10% 1989: ~0.10% 1993: ~0.10% 1994: ~0.10% 1996: ~0.10% 2013: ~0.10% 2043: ~0.10% 2045: ~0.10% 2054: ~0.10% 2070: ~0.10% 2074: ~0.10% 2076: ~0.10% 2080: ~0.10% 2083: ~0.10% 2084: ~0.10% 2087: ~0.10% 2095: ~0.10% 2116: ~0.10% 2124: ~0.10% 2125: ~0.10% 2126: ~0.10% 2129: ~0.10% 2131: ~0.10% 2133: ~0.10% 2137: ~0.10% 2154: ~0.20% 2157: ~0.10% 2160: ~0.10% 2162: ~0.10% 2168: ~0.10% 2177: ~0.10% 2181: ~0.10% 2184: ~0.10% 2193: ~0.10% 2194: ~0.10% 2195: ~0.10% 2196: ~0.10% 2200: ~0.10% 2201: ~0.10% 2202: ~0.10% 2203: ~0.10% 2215: ~0.10% 2217: ~0.10% 2226: ~0.10% 2228: ~0.10% 2231: ~0.10% 2232: ~0.10% 2235: ~0.10% 2236: ~0.10% 2238: ~0.10% 2242: ~0.10% 2243: ~0.10% 2244: ~0.10% 2246: ~0.10% 2247: ~0.10% 2250: ~0.10% 2253: ~0.10% 2261: ~0.10% 2263: ~0.10% 2272: ~0.10% 2275: ~0.10% 2284: ~0.10% 2292: ~0.10% 2297: ~0.10% 2298: ~0.10% 2299: ~0.10% 2300: ~0.10% 2315: ~0.10% 2316: ~0.10% 2326: ~0.10% 2337: ~0.10% 2341: ~0.10% 2346: ~0.10% 2350: ~0.10% 2353: ~0.10% 2354: ~0.10% 2356: ~0.20% 2357: ~0.10% 2359: ~0.10% 2368: ~0.10% 2377: ~0.10% 2384: ~0.10% 2389: ~0.10% 2390: ~0.10% 2391: ~0.10% 2400: ~0.10% 2408: ~0.10% 2409: ~0.10% 2412: ~0.10% 2420: ~0.10% 2423: ~0.10% 2439: ~0.10% 2442: ~0.10% 2444: ~0.10% 2453: ~0.10% 2454: ~0.10% 2458: ~0.10% 2464: ~0.10% 2475: ~0.10% 2477: ~0.10% 2481: ~0.10% 2482: ~0.10% 2491: ~0.10% 2498: ~0.10% 2500: ~0.20% 2508: ~0.10% 2526: ~0.20% 2529: ~0.10% 2538: ~0.10% 2540: ~0.10% 2544: ~0.20% 2546: ~0.10% 2554: ~0.10% 2555: ~0.10% 2557: ~0.10% 2561: ~0.10% 2562: ~0.10% 2565: ~0.10% 2568: ~0.10% 2572: ~0.10% 2582: ~0.10% 2583: ~0.10% 2586: ~0.10% 2587: ~0.10% 2588: ~0.10% 2597: ~0.10% 2598: ~0.10% 2605: ~0.10% 2616: ~0.10% 2618: ~0.10% 2622: ~0.10% 2637: ~0.10% 2639: ~0.10% 2646: ~0.10% 2647: ~0.10% 2648: ~0.10% 2649: ~0.10% 2654: ~0.20% 2664: ~0.10% 2667: ~0.10% 2668: ~0.10% 2676: ~0.10% 2678: ~0.10% 2679: ~0.10% 2681: ~0.10% 2682: ~0.10% 2688: ~0.10% 2695: ~0.10% 2698: ~0.10% 2708: ~0.10% 2713: ~0.10% 2715: ~0.10% 2718: ~0.10% 2721: ~0.20% 2722: ~0.10% 2730: ~0.10% 2734: ~0.10% 2736: ~0.10% 2737: ~0.10% 2745: ~0.10% 2766: ~0.20% 2788: ~0.10% 2793: ~0.10% 2794: ~0.10% 2796: ~0.10% 2799: ~0.10% 2801: ~0.10% 2813: ~0.10% 2819: ~0.10% 2823: ~0.10% 2827: ~0.10% 2831: ~0.10% 2838: ~0.10% 2846: ~0.10% 2848: ~0.10% 2850: ~0.10% 2852: ~0.10% 2853: ~0.10% 2854: ~0.10% 2861: ~0.10% 2868: ~0.10% 2869: ~0.10% 2872: ~0.10% 2875: ~0.10% 2878: ~0.10% 2880: ~0.10% 2889: ~0.10% 2902: ~0.10% 2904: ~0.10% 2906: ~0.10% 2910: ~0.10% 2914: ~0.10% 2917: ~0.10% 2923: ~0.10% 2930: ~0.10% 2945: ~0.10% 2950: ~0.10% 2951: ~0.10% 2954: ~0.10% 2963: ~0.10% 2968: ~0.10% 2971: ~0.10% 2973: ~0.10% 2974: ~0.10% 3002: ~0.10% 3013: ~0.10% 3014: ~0.10% 3018: ~0.10% 3021: ~0.10% 3027: ~0.10% 3037: ~0.10% 3042: ~0.10% 3048: ~0.10% 3051: ~0.10% 3053: ~0.10% 3060: ~0.10% 3061: ~0.10% 3075: ~0.20% 3080: ~0.10% 3085: ~0.10% 3090: ~0.10% 3092: ~0.10% 3095: ~0.10% 3098: ~0.10% 3106: ~0.10% 3120: ~0.10% 3121: ~0.10% 3123: ~0.10% 3133: ~0.10% 3142: ~0.10% 3144: ~0.20% 3154: ~0.10% 3162: ~0.10% 3163: ~0.10% 3171: ~0.10% 3175: ~0.10% 3178: ~0.10% 3189: ~0.10% 3191: ~0.10% 3196: ~0.10% 3219: ~0.10% 3221: ~0.10% 3228: ~0.10% 3229: ~0.10% 3230: ~0.10% 3233: ~0.10% 3246: ~0.10% 3255: ~0.10% 3257: ~0.10% 3258: ~0.10% 3260: ~0.10% 3261: ~0.10% 3263: ~0.10% 3264: ~0.10% 3267: ~0.10% 3271: ~0.10% 3272: ~0.10% 3285: ~0.10% 3289: ~0.10% 3292: ~0.10% 3306: ~0.10% 3308: ~0.10% 3311: ~0.10% 3343: ~0.10% 3358: ~0.10% 3359: ~0.10% 3376: ~0.10% 3378: ~0.10% 3380: ~0.10% 3381: ~0.10% 3395: ~0.10% 3396: ~0.10% 3397: ~0.10% 3398: ~0.10% 3402: ~0.10% 3406: ~0.10% 3407: ~0.10% 3410: ~0.10% 3424: ~0.10% 3425: ~0.10% 3426: ~0.10% 3427: ~0.10% 3434: ~0.10% 3437: ~0.10% 3442: ~0.10% 3444: ~0.10% 3450: ~0.20% 3458: ~0.10% 3460: ~0.10% 3471: ~0.10% 3480: ~0.10% 3486: ~0.10% 3495: ~0.10% 3505: ~0.10% 3519: ~0.10% 3522: ~0.10% 3523: ~0.10% 3530: ~0.10% 3547: ~0.10% 3548: ~0.10% 3549: ~0.10% 3551: ~0.10% 3560: ~0.10% 3566: ~0.10% 3567: ~0.10% 3571: ~0.10% 3573: ~0.10% 3578: ~0.10% 3585: ~0.10% 3589: ~0.10% 3590: ~0.10% 3594: ~0.10% 3596: ~0.10% 3598: ~0.10% 3605: ~0.10% 3607: ~0.10% 3608: ~0.10% 3619: ~0.10% 3624: ~0.10% 3630: ~0.10% 3632: ~0.20% 3633: ~0.10% 3634: ~0.10% 3637: ~0.10% 3653: ~0.10% 3654: ~0.10% 3656: ~0.10% 3657: ~0.10% 3661: ~0.10% 3668: ~0.10% 3679: ~0.10% 3681: ~0.10% 3687: ~0.10% 3690: ~0.10% 3693: ~0.10% 3695: ~0.10% 3717: ~0.10% 3719: ~0.10% 3725: ~0.10% 3727: ~0.20% 3729: ~0.10% 3735: ~0.10% 3737: ~0.10% 3744: ~0.10% 3745: ~0.10% 3747: ~0.10% 3750: ~0.10% 3756: ~0.10% 3758: ~0.10% 3768: ~0.10% 3773: ~0.10% 3774: ~0.10% 3786: ~0.10% 3793: ~0.20% 3794: ~0.10% 3799: ~0.10% 3800: ~0.10% 3806: ~0.10% 3809: ~0.10% 3814: ~0.20% 3817: ~0.20% 3822: ~0.10% 3826: ~0.10% 3827: ~0.10% 3830: ~0.10% 3835: ~0.10% 3837: ~0.10% 3838: ~0.10% 3839: ~0.10% 3848: ~0.10% 3852: ~0.10% 3853: ~0.10% 3854: ~0.10% 3855: ~0.10% 3860: ~0.10% 3868: ~0.10% 3886: ~0.10% 3887: ~0.10% 3888: ~0.10% 3901: ~0.10% 3904: ~0.10% 3905: ~0.10% 3907: ~0.10% 3916: ~0.10% 3918: ~0.10% 3923: ~0.10% 3928: ~0.10% 3930: ~0.10% 3932: ~0.10% 3934: ~0.10% 3938: ~0.10% 3939: ~0.10% 3943: ~0.10% 3952: ~0.10% 3956: ~0.10% 3961: ~0.10% 3969: ~0.10% 3972: ~0.20% 3977: ~0.10% 3981: ~0.10% 3986: ~0.10% 3998: ~0.10% 4006: ~0.10% 4011: ~0.10% 4024: ~0.10% 4030: ~0.10% 4031: ~0.10% 4032: ~0.10% 4042: ~0.10% 4046: ~0.10% 4062: ~0.20% 4070: ~0.10% 4071: ~0.10% 4074: ~0.10% 4077: ~0.10% 4079: ~0.10% 4080: ~0.10% 4085: ~0.10% 4098: ~0.10% 4109: ~0.10% 4119: ~0.10% 4120: ~0.10% 4124: ~0.10% 4130: ~0.10% 4135: ~0.10% 4139: ~0.10% 4140: ~0.10% 4150: ~0.10% 4158: ~0.10% 4164: ~0.10% 4167: ~0.10% 4169: ~0.10% 4174: ~0.10% 4183: ~0.10% 4184: ~0.10% 4185: ~0.10% 4197: ~0.10% 4202: ~0.10% 4204: ~0.10% 4212: ~0.10% 4220: ~0.10% 4228: ~0.10% 4230: ~0.10% 4234: ~0.10% 4235: ~0.10% 4236: ~0.10% 4238: ~0.10% 4243: ~0.10% 4256: ~0.10% 4257: ~0.10% 4261: ~0.10% 4278: ~0.10% 4282: ~0.10% 4283: ~0.10% 4284: ~0.10% 4290: ~0.10% 4295: ~0.10% 4318: ~0.10% 4323: ~0.10% 4326: ~0.10% 4328: ~0.10% 4341: ~0.10% 4353: ~0.10% 4357: ~0.20% 4371: ~0.20% 4385: ~0.10% 4390: ~0.10% 4395: ~0.10% 4400: ~0.10% 4403: ~0.10% 4409: ~0.10% 4411: ~0.10% 4413: ~0.10% 4423: ~0.10% 4425: ~0.10% 4426: ~0.10% 4431: ~0.10% 4432: ~0.10% 4439: ~0.10% 4444: ~0.10% 4455: ~0.10% 4461: ~0.10% 4468: ~0.10% 4471: ~0.10% 4475: ~0.10% 4493: ~0.10% 4504: ~0.10% 4505: ~0.20% 4513: ~0.10% 4515: ~0.10% 4532: ~0.10% 4552: ~0.10% 4553: ~0.10% 4555: ~0.10% 4564: ~0.10% 4567: ~0.10% 4578: ~0.10% 4580: ~0.10% 4600: ~0.10% 4604: ~0.10% 4616: ~0.10% 4619: ~0.10% 4637: ~0.10% 4638: ~0.10% 4648: ~0.10% 4653: ~0.10% 4665: ~0.10% 4670: ~0.10% 4681: ~0.10% 4685: ~0.20% 4690: ~0.10% 4695: ~0.10% 4696: ~0.10% 4697: ~0.10% 4698: ~0.20% 4699: ~0.10% 4706: ~0.10% 4712: ~0.10% 4720: ~0.10% 4724: ~0.10% 4731: ~0.10% 4733: ~0.10% 4739: ~0.10% 4740: ~0.10% 4747: ~0.10% 4748: ~0.10% 4755: ~0.10% 4766: ~0.10% 4780: ~0.10% 4789: ~0.10% 4792: ~0.10% 4808: ~0.10% 4815: ~0.10% 4817: ~0.10% 4822: ~0.10% 4825: ~0.10% 4838: ~0.10% 4839: ~0.10% 4843: ~0.10% 4858: ~0.10% 4862: ~0.10% 4867: ~0.10% 4869: ~0.10% 4871: ~0.10% 4872: ~0.10% 4877: ~0.10% 4886: ~0.10% 4894: ~0.10% 4898: ~0.10% 4900: ~0.10% 4903: ~0.10% 4904: ~0.10% 4907: ~0.10% 4923: ~0.10% 4930: ~0.10% 4931: ~0.10% 4933: ~0.10% 4940: ~0.10% 4942: ~0.10% 4944: ~0.10% 4946: ~0.10% 4952: ~0.10% 4954: ~0.10% 4957: ~0.10% 4969: ~0.10% 4979: ~0.10% 4982: ~0.10% 4986: ~0.10% 5002: ~0.10% 5005: ~0.10% 5014: ~0.10% 5018: ~0.10% 5020: ~0.10% 5034: ~0.10% 5040: ~0.20% 5043: ~0.10% 5046: ~0.20% 5048: ~0.10% 5051: ~0.10% 5058: ~0.10% 5064: ~0.10% 5066: ~0.10% 5072: ~0.10% 5075: ~0.10% 5076: ~0.10% 5082: ~0.10% 5087: ~0.10% 5101: ~0.10% 5102: ~0.10% 5106: ~0.10% 5110: ~0.10% 5124: ~0.10% 5133: ~0.10% 5135: ~0.20% 5139: ~0.10% 5140: ~0.10% 5170: ~0.20% 5175: ~0.10% 5196: ~0.10% 5204: ~0.10% 5214: ~0.10% 5222: ~0.10% 5229: ~0.10% 5243: ~0.10% 5261: ~0.10% 5265: ~0.10% 5273: ~0.10% 5279: ~0.10% 5280: ~0.10% 5283: ~0.10% 5285: ~0.10% 5286: ~0.10% 5287: ~0.10% 5288: ~0.10% 5311: ~0.10% 5314: ~0.10% 5315: ~0.10% 5318: ~0.10% 5322: ~0.10% 5323: ~0.10% 5329: ~0.10% 5331: ~0.10% 5333: ~0.10% 5334: ~0.10%

• Samples:

  text	label
  Integrated radio receiver with sound recording capabilities, mobile battery-operated system.	4681
  Premium frozen turkey, 7-9 kg, antibiotic-free, processed within 24 hours.	69
  Glittering colored granules, under 7mm, used in cosmetics and decorative arts.	3263

• Loss: BatchHardTripletLoss

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• learning_rate: 1e-05
• warmup_ratio: 0.1
• bf16: True
• optim: adamw_torch_fused
• hub_private_repo: True
• batch_sampler: group_by_label

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 1e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 3
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: True
• fp16: False
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch_fused
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: True
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• dispatch_batches: None
• split_batches: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: group_by_label
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	eval_cosine_accuracy
0	0	-	-	0.8447
0.0037	10	4.0341	-	-
0.0075	20	3.8755	-	-
0.0112	30	3.9669	-	-
0.0150	40	4.6424	-	-
0.0187	50	3.4661	-	-
0.0225	60	3.7177	-	-
0.0262	70	4.5446	-	-
0.0300	80	3.2709	-	-
0.0337	90	4.648	-	-
0.0375	100	2.4594	-	-
0.0412	110	2.4992	-	-
0.0449	120	2.381	-	-
0.0487	130	2.9117	-	-
0.0524	140	2.0562	-	-
0.0562	150	2.9831	-	-
0.0599	160	3.3428	-	-
0.0637	170	3.3217	-	-
0.0674	180	3.5566	-	-
0.0712	190	3.0018	-	-
0.0749	200	2.4643	-	-
0.0787	210	2.1375	-	-
0.0824	220	2.7643	-	-
0.0861	230	2.3066	-	-
0.0899	240	2.0659	-	-
0.0936	250	1.6675	-	-
0.0974	260	2.516	-	-
0.1011	270	1.4495	-	-
0.1049	280	3.1037	-	-
0.1086	290	2.9175	-	-
0.1124	300	2.8179	-	-
0.1161	310	1.8993	-	-
0.1199	320	2.7167	-	-
0.1236	330	2.0482	-	-
0.1273	340	3.0799	-	-
0.1311	350	3.0152	-	-
0.1348	360	2.4402	-	-
0.1386	370	1.7145	-	-
0.1423	380	1.5029	-	-
0.1461	390	2.3034	-	-
0.1498	400	2.0296	-	-
0.1536	410	2.3206	-	-
0.1573	420	2.3162	-	-
0.1610	430	1.3744	-	-
0.1648	440	2.9439	-	-
0.1685	450	2.5834	-	-
0.1723	460	2.2302	-	-
0.1760	470	1.5165	-	-
0.1798	480	2.4553	-	-
0.1835	490	1.6331	-	-
0.1873	500	1.7502	-	-
0.1910	510	2.3556	-	-
0.1948	520	2.6268	-	-
0.1985	530	2.3735	-	-
0.2022	540	2.9494	-	-
0.2060	550	1.7133	-	-
0.2097	560	1.5455	-	-
0.2135	570	1.8857	-	-
0.2172	580	1.7242	-	-
0.2210	590	0.9303	-	-
0.2247	600	1.2073	-	-
0.2285	610	1.9799	-	-
0.2322	620	0.5134	-	-
0.2360	630	1.7473	-	-
0.2397	640	2.5535	-	-
0.2434	650	2.2415	-	-
0.2472	660	2.3361	-	-
0.2509	670	2.1372	-	-
0.2547	680	1.8236	-	-
0.2584	690	1.7999	-	-
0.2622	700	1.041	-	-
0.2659	710	1.5633	-	-
0.2697	720	1.475	-	-
0.2734	730	2.6768	-	-
0.2772	740	2.0162	-	-
0.2809	750	2.8179	-	-
0.2846	760	2.2107	-	-
0.2884	770	1.4401	-	-
0.2921	780	1.3463	-	-
0.2959	790	1.4704	-	-
0.2996	800	2.1911	-	-
0.3034	810	1.4399	-	-
0.3071	820	1.6818	-	-
0.3109	830	1.3086	-	-
0.3146	840	3.0084	-	-
0.3184	850	1.5507	-	-
0.3221	860	1.2379	-	-
0.3258	870	1.6205	-	-
0.3296	880	1.7312	-	-
0.3333	890	1.2205	-	-
0.3371	900	2.0977	-	-
0.3408	910	2.1105	-	-
0.3446	920	1.6375	-	-
0.3483	930	1.7065	-	-
0.3521	940	1.6578	-	-
0.3558	950	1.9871	-	-
0.3596	960	2.5589	-	-
0.3633	970	1.5536	-	-
0.3670	980	1.5662	-	-
0.3708	990	2.3202	-	-
0.3745	1000	1.6294	-	-
0.3783	1010	2.0777	-	-
0.3820	1020	1.5202	-	-
0.3858	1030	1.8365	-	-
0.3895	1040	0.9917	-	-
0.3933	1050	1.3668	-	-
0.3970	1060	1.0952	-	-
0.4007	1070	0.6225	-	-
0.4045	1080	0.9056	-	-
0.4082	1090	2.5108	-	-
0.4120	1100	0.8275	-	-
0.4157	1110	0.8328	-	-
0.4195	1120	1.6204	-	-
0.4232	1130	1.4578	-	-
0.4270	1140	0.985	-	-
0.4307	1150	1.5583	-	-
0.4345	1160	0.797	-	-
0.4382	1170	1.2212	-	-
0.4419	1180	1.3289	-	-
0.4457	1190	1.4719	-	-
0.4494	1200	0.9898	-	-
0.4532	1210	1.5724	-	-
0.4569	1220	2.4698	-	-
0.4607	1230	1.7312	-	-
0.4644	1240	0.8984	-	-
0.4682	1250	1.4435	-	-
0.4719	1260	0.4182	-	-
0.4757	1270	2.5585	-	-
0.4794	1280	2.1777	-	-
0.4831	1290	1.8817	-	-
0.4869	1300	1.3328	-	-
0.4906	1310	1.1548	-	-
0.4944	1320	1.8619	-	-
0.4981	1330	1.8818	-	-
0.5019	1340	1.2547	-	-
0.5056	1350	1.1262	-	-
0.5094	1360	2.4004	-	-
0.5131	1370	0.5397	-	-
0.5169	1380	1.1227	-	-
0.5206	1390	2.1331	-	-
0.5243	1400	0.8593	-	-
0.5281	1410	1.7893	-	-
0.5318	1420	0.5693	-	-
0.5356	1430	1.0304	-	-
0.5393	1440	0.7579	-	-
0.5431	1450	1.5615	-	-
0.5468	1460	0.6529	-	-
0.5506	1470	0.5767	-	-
0.5543	1480	1.3396	-	-
0.5581	1490	1.2152	-	-
0.5618	1500	0.8144	-	-
0.5655	1510	2.0135	-	-
0.5693	1520	2.5916	-	-
0.5730	1530	1.553	-	-
0.5768	1540	0.6537	-	-
0.5805	1550	0.7982	-	-
0.5843	1560	1.9476	-	-
0.5880	1570	0.6488	-	-
0.5918	1580	1.0492	-	-
0.5955	1590	1.7359	-	-
0.5993	1600	2.0695	-	-
0.6030	1610	0.7046	-	-
0.6067	1620	1.1444	-	-
0.6105	1630	0.9934	-	-
0.6142	1640	0.5541	-	-
0.6180	1650	0.9048	-	-
0.6217	1660	1.9154	-	-
0.6255	1670	2.3706	-	-
0.6292	1680	0.2856	-	-
0.6330	1690	1.0283	-	-
0.6367	1700	1.2681	-	-
0.6404	1710	0.9028	-	-
0.6442	1720	0.9902	-	-
0.6479	1730	1.3535	-	-
0.6517	1740	0.9419	-	-
0.6554	1750	0.9893	-	-
0.6592	1760	1.4345	-	-
0.6629	1770	2.1841	-	-
0.6667	1780	0.7408	-	-
0.6704	1790	2.4774	-	-
0.6742	1800	0.7757	-	-
0.6779	1810	2.0088	-	-
0.6816	1820	1.5048	-	-
0.6854	1830	0.9138	-	-
0.6891	1840	1.403	-	-
0.6929	1850	1.5927	-	-
0.6966	1860	1.0471	-	-
0.7004	1870	1.6628	-	-
0.7041	1880	0.6006	-	-
0.7079	1890	0.2351	-	-
0.7116	1900	0.9406	-	-
0.7154	1910	1.5868	-	-
0.7191	1920	1.1405	-	-
0.7228	1930	0.2823	-	-
0.7266	1940	1.7329	-	-
0.7303	1950	1.7973	-	-
0.7341	1960	0.9928	-	-
0.7378	1970	1.8539	-	-
0.7416	1980	1.7418	-	-
0.7453	1990	1.7236	-	-
0.7491	2000	0.7957	-	-
0.7528	2010	0.0987	-	-
0.7566	2020	1.7363	-	-
0.7603	2030	0.8135	-	-
0.7640	2040	1.7698	-	-
0.7678	2050	1.4394	-	-
0.7715	2060	0.7707	-	-
0.7753	2070	2.7317	-	-
0.7790	2080	0.3891	-	-
0.7828	2090	2.6116	-	-
0.7865	2100	1.1891	-	-
0.7903	2110	1.5366	-	-
0.7940	2120	0.4196	-	-
0.7978	2130	0.745	-	-
0.8015	2140	1.4042	-	-
0.8052	2150	2.7567	-	-
0.8090	2160	1.9903	-	-
0.8127	2170	1.8249	-	-
0.8165	2180	2.0049	-	-
0.8202	2190	1.6193	-	-
0.8240	2200	1.0768	-	-
0.8277	2210	1.5331	-	-
0.8315	2220	0.8109	-	-
0.8352	2230	0.6081	-	-
0.8390	2240	1.3533	-	-
0.8427	2250	2.0449	-	-
0.8464	2260	1.1876	-	-
0.8502	2270	0.7197	-	-
0.8539	2280	0.9462	-	-
0.8577	2290	0.7562	-	-
0.8614	2300	0.9699	-	-
0.8652	2310	1.115	-	-
0.8689	2320	0.9679	-	-
0.8727	2330	2.0255	-	-
0.8764	2340	0.7457	-	-
0.8801	2350	0.7221	-	-
0.8839	2360	1.4877	-	-
0.8876	2370	1.0071	-	-
0.8914	2380	1.0958	-	-
0.8951	2390	1.2945	-	-
0.8989	2400	1.5245	-	-
0.9026	2410	0.7008	-	-
0.9064	2420	1.5043	-	-
0.9101	2430	1.3202	-	-
0.9139	2440	1.2748	-	-
0.9176	2450	1.3845	-	-
0.9213	2460	1.2619	-	-
0.9251	2470	1.1196	-	-
0.9288	2480	2.1311	-	-
0.9326	2490	1.0909	-	-
0.9363	2500	2.2843	-	-
0.9401	2510	0.4763	-	-
0.9438	2520	1.4252	-	-
0.9476	2530	1.6419	-	-
0.9513	2540	1.6628	-	-
0.9551	2550	1.0937	-	-
0.9588	2560	2.9318	-	-
0.9625	2570	1.3356	-	-
0.9663	2580	1.6431	-	-
0.9700	2590	2.4785	-	-
0.9738	2600	2.0959	-	-
0.9775	2610	1.021	-	-
0.9813	2620	1.5177	-	-
0.9850	2630	1.1866	-	-
0.9888	2640	1.5121	-	-
0.9925	2650	0.9855	-	-
0.9963	2660	0.9257	-	-
1.0	2670	0.9811	1.0522	0.8691
1.0037	2680	0.4769	-	-
1.0075	2690	0.0788	-	-
1.0112	2700	0.4716	-	-
1.0150	2710	0.6434	-	-
1.0187	2720	0.3483	-	-
1.0225	2730	1.1349	-	-
1.0262	2740	1.4718	-	-
1.0300	2750	0.6267	-	-
1.0337	2760	0.7566	-	-
1.0375	2770	0.5439	-	-
1.0412	2780	1.3736	-	-
1.0449	2790	0.482	-	-
1.0487	2800	0.8668	-	-
1.0524	2810	1.5906	-	-
1.0562	2820	1.5024	-	-
1.0599	2830	1.1421	-	-
1.0637	2840	0.951	-	-
1.0674	2850	1.2362	-	-
1.0712	2860	0.9021	-	-
1.0749	2870	0.6175	-	-
1.0787	2880	0.5354	-	-
1.0824	2890	0.8739	-	-
1.0861	2900	1.2778	-	-
1.0899	2910	1.1148	-	-
1.0936	2920	1.2744	-	-
1.0974	2930	2.8342	-	-
1.1011	2940	0.8226	-	-
1.1049	2950	0.7788	-	-
1.1086	2960	0.2087	-	-
1.1124	2970	2.0295	-	-
1.1161	2980	0.7227	-	-
1.1199	2990	0.3996	-	-
1.1236	3000	1.081	-	-
1.1273	3010	1.1544	-	-
1.1311	3020	1.4191	-	-
1.1348	3030	0.9023	-	-
1.1386	3040	1.2946	-	-
1.1423	3050	0.7664	-	-
1.1461	3060	1.8775	-	-
1.1498	3070	1.1414	-	-
1.1536	3080	1.4882	-	-
1.1573	3090	0.9656	-	-
1.1610	3100	0.254	-	-
1.1648	3110	2.8362	-	-
1.1685	3120	1.5211	-	-
1.1723	3130	0.5995	-	-
1.1760	3140	1.192	-	-
1.1798	3150	0.5996	-	-
1.1835	3160	0.9875	-	-
1.1873	3170	0.9348	-	-
1.1910	3180	0.8946	-	-
1.1948	3190	1.2509	-	-
1.1985	3200	1.5223	-	-
1.2022	3210	1.6398	-	-
1.2060	3220	1.2502	-	-
1.2097	3230	1.713	-	-
1.2135	3240	0.2114	-	-
1.2172	3250	0.7086	-	-
1.2210	3260	1.3041	-	-
1.2247	3270	1.2593	-	-
1.2285	3280	0.4046	-	-
1.2322	3290	1.2122	-	-
1.2360	3300	1.3019	-	-
1.2397	3310	0.7197	-	-
1.2434	3320	0.6891	-	-
1.2472	3330	0.7012	-	-
1.2509	3340	1.0261	-	-
1.2547	3350	1.2433	-	-
1.2584	3360	0.1486	-	-
1.2622	3370	0.1235	-	-
1.2659	3380	1.5325	-	-
1.2697	3390	0.7763	-	-
1.2734	3400	1.6514	-	-
1.2772	3410	1.3432	-	-
1.2809	3420	0.9633	-	-
1.2846	3430	0.5197	-	-
1.2884	3440	1.5208	-	-
1.2921	3450	0.1065	-	-
1.2959	3460	1.158	-	-
1.2996	3470	0.1859	-	-
1.3034	3480	0.5727	-	-
1.3071	3490	0.4956	-	-
1.3109	3500	1.7412	-	-
1.3146	3510	1.0473	-	-
1.3184	3520	1.1178	-	-
1.3221	3530	2.0815	-	-
1.3258	3540	2.2776	-	-
1.3296	3550	0.7169	-	-
1.3333	3560	1.3027	-	-
1.3371	3570	1.7225	-	-
1.3408	3580	0.7588	-	-
1.3446	3590	0.7847	-	-
1.3483	3600	0.9037	-	-
1.3521	3610	1.3455	-	-
1.3558	3620	0.9022	-	-
1.3596	3630	0.1956	-	-
1.3633	3640	1.0445	-	-
1.3670	3650	0.8999	-	-
1.3708	3660	0.439	-	-
1.3745	3670	1.1256	-	-
1.3783	3680	0.8729	-	-
1.3820	3690	2.2068	-	-
1.3858	3700	1.6487	-	-
1.3895	3710	0.9254	-	-
1.3933	3720	0.2883	-	-
1.3970	3730	0.8981	-	-
1.4007	3740	1.2252	-	-
1.4045	3750	0.8682	-	-
1.4082	3760	0.8365	-	-
1.4120	3770	1.8876	-	-
1.4157	3780	0.6073	-	-
1.4195	3790	0.9617	-	-
1.4232	3800	0.2706	-	-
1.4270	3810	0.3518	-	-
1.4307	3820	1.1181	-	-
1.4345	3830	1.2088	-	-
1.4382	3840	0.8219	-	-
1.4419	3850	1.0337	-	-
1.4457	3860	1.5798	-	-
1.4494	3870	0.293	-	-
1.4532	3880	0.577	-	-
1.4569	3890	1.1591	-	-
1.4607	3900	0.677	-	-
1.4644	3910	0.2807	-	-
1.4682	3920	0.8355	-	-
1.4719	3930	1.1842	-	-
1.4757	3940	1.1249	-	-
1.4794	3950	0.9494	-	-
1.4831	3960	0.3435	-	-
1.4869	3970	0.491	-	-
1.4906	3980	0.024	-	-
1.4944	3990	0.4431	-	-
1.4981	4000	0.3127	-	-
1.5019	4010	1.1624	-	-
1.5056	4020	0.7637	-	-
1.5094	4030	0.2917	-	-
1.5131	4040	0.5337	-	-
1.5169	4050	0.4679	-	-
1.5206	4060	1.1765	-	-
1.5243	4070	1.5454	-	-
1.5281	4080	1.1035	-	-
1.5318	4090	0.4787	-	-
1.5356	4100	1.1475	-	-
1.5393	4110	2.5765	-	-
1.5431	4120	0.8925	-	-
1.5468	4130	1.1461	-	-
1.5506	4140	1.0587	-	-
1.5543	4150	0.8122	-	-
1.5581	4160	1.197	-	-
1.5618	4170	1.5496	-	-
1.5655	4180	0.5243	-	-
1.5693	4190	1.1577	-	-
1.5730	4200	0.8121	-	-
1.5768	4210	0.623	-	-
1.5805	4220	0.7428	-	-
1.5843	4230	1.3538	-	-
1.5880	4240	0.5452	-	-
1.5918	4250	0.6693	-	-
1.5955	4260	0.5567	-	-
1.5993	4270	1.1811	-	-
1.6030	4280	0.5026	-	-
1.6067	4290	0.8282	-	-
1.6105	4300	1.3515	-	-
1.6142	4310	1.0876	-	-
1.6180	4320	1.3355	-	-
1.6217	4330	0.7432	-	-
1.6255	4340	0.7268	-	-
1.6292	4350	2.156	-	-
1.6330	4360	0.5804	-	-
1.6367	4370	0.5645	-	-
1.6404	4380	0.3972	-	-
1.6442	4390	0.3717	-	-
1.6479	4400	0.3682	-	-
1.6517	4410	0.8165	-	-
1.6554	4420	0.4629	-	-
1.6592	4430	0.4669	-	-
1.6629	4440	1.4872	-	-
1.6667	4450	0.0391	-	-
1.6704	4460	0.5723	-	-
1.6742	4470	0.1429	-	-
1.6779	4480	1.3683	-	-
1.6816	4490	0.2154	-	-
1.6854	4500	0.486	-	-
1.6891	4510	0.57	-	-
1.6929	4520	0.4862	-	-
1.6966	4530	0.7939	-	-
1.7004	4540	1.6848	-	-
1.7041	4550	0.7317	-	-
1.7079	4560	0.9226	-	-
1.7116	4570	0.9461	-	-
1.7154	4580	0.5289	-	-
1.7191	4590	0.9467	-	-
1.7228	4600	0.4374	-	-
1.7266	4610	0.8408	-	-
1.7303	4620	0.7935	-	-
1.7341	4630	0.8529	-	-
1.7378	4640	0.9103	-	-
1.7416	4650	0.8169	-	-
1.7453	4660	0.7316	-	-
1.7491	4670	0.3014	-	-
1.7528	4680	1.0149	-	-
1.7566	4690	1.1554	-	-
1.7603	4700	0.9175	-	-
1.7640	4710	0.332	-	-
1.7678	4720	1.0431	-	-
1.7715	4730	0.4539	-	-
1.7753	4740	0.3434	-	-
1.7790	4750	1.6847	-	-
1.7828	4760	0.6125	-	-
1.7865	4770	0.6509	-	-
1.7903	4780	2.1171	-	-
1.7940	4790	0.1296	-	-
1.7978	4800	0.8468	-	-
1.8015	4810	0.8887	-	-
1.8052	4820	0.475	-	-
1.8090	4830	1.1306	-	-
1.8127	4840	1.56	-	-
1.8165	4850	1.446	-	-
1.8202	4860	1.1175	-	-
1.8240	4870	1.5735	-	-
1.8277	4880	1.749	-	-
1.8315	4890	0.6597	-	-
1.8352	4900	0.8736	-	-
1.8390	4910	0.2586	-	-
1.8427	4920	1.0175	-	-
1.8464	4930	1.0651	-	-
1.8502	4940	0.3644	-	-
1.8539	4950	0.7849	-	-
1.8577	4960	1.4129	-	-
1.8614	4970	1.3896	-	-
1.8652	4980	0.5037	-	-
1.8689	4990	0.2482	-	-
1.8727	5000	1.1326	-	-
1.8764	5010	0.7214	-	-
1.8801	5020	0.7837	-	-
1.8839	5030	1.9915	-	-
1.8876	5040	1.0516	-	-
1.8914	5050	0.8879	-	-
1.8951	5060	1.6854	-	-
1.8989	5070	1.313	-	-
1.9026	5080	0.5719	-	-
1.9064	5090	0.2045	-	-
1.9101	5100	0.4238	-	-
1.9139	5110	0.8916	-	-
1.9176	5120	0.9572	-	-
1.9213	5130	0.9926	-	-
1.9251	5140	1.3111	-	-
1.9288	5150	0.7925	-	-
1.9326	5160	0.8453	-	-
1.9363	5170	0.2731	-	-
1.9401	5180	1.3019	-	-
1.9438	5190	1.2677	-	-
1.9476	5200	1.5136	-	-
1.9513	5210	1.4283	-	-
1.9551	5220	1.4765	-	-
1.9588	5230	0.3049	-	-
1.9625	5240	0.988	-	-
1.9663	5250	1.7154	-	-
1.9700	5260	0.5865	-	-
1.9738	5270	0.8685	-	-
1.9775	5280	2.1119	-	-
1.9813	5290	1.6986	-	-
1.9850	5300	0.9968	-	-
1.9888	5310	0.6045	-	-
1.9925	5320	0.7844	-	-
1.9963	5330	0.7483	-	-
2.0	5340	2.4421	0.8997	0.8694
2.0037	5350	0.3721	-	-
2.0075	5360	0.7311	-	-
2.0112	5370	0.4219	-	-
2.0150	5380	0.5756	-	-
2.0187	5390	0.2848	-	-
2.0225	5400	0.7341	-	-
2.0262	5410	0.4964	-	-
2.0300	5420	0.1535	-	-
2.0337	5430	0.4309	-	-
2.0375	5440	0.3544	-	-
2.0412	5450	0.2336	-	-
2.0449	5460	1.212	-	-
2.0487	5470	0.5154	-	-
2.0524	5480	0.1163	-	-
2.0562	5490	0.9765	-	-
2.0599	5500	0.2086	-	-
2.0637	5510	0.2978	-	-
2.0674	5520	1.9357	-	-
2.0712	5530	0.6232	-	-
2.0749	5540	0.6823	-	-
2.0787	5550	0.0296	-	-
2.0824	5560	0.9172	-	-
2.0861	5570	0.3007	-	-
2.0899	5580	0.4675	-	-
2.0936	5590	0.1491	-	-
2.0974	5600	1.1711	-	-
2.1011	5610	0.6131	-	-
2.1049	5620	0.0001	-	-
2.1086	5630	0.408	-	-
2.1124	5640	0.0041	-	-
2.1161	5650	0.2059	-	-
2.1199	5660	0.675	-	-
2.1236	5670	0.6992	-	-
2.1273	5680	0.3526	-	-
2.1311	5690	0.2875	-	-
2.1348	5700	0.6462	-	-
2.1386	5710	0.3409	-	-
2.1423	5720	0.4659	-	-
2.1461	5730	0.4404	-	-
2.1498	5740	0.9744	-	-
2.1536	5750	0.5018	-	-
2.1573	5760	0.5624	-	-
2.1610	5770	0.9155	-	-
2.1648	5780	0.1129	-	-
2.1685	5790	0.0347	-	-
2.1723	5800	1.0591	-	-
2.1760	5810	0.0432	-	-
2.1798	5820	0.041	-	-
2.1835	5830	0.2072	-	-
2.1873	5840	0.3379	-	-
2.1910	5850	0.3583	-	-
2.1948	5860	0.4276	-	-
2.1985	5870	0.4756	-	-
2.2022	5880	1.0452	-	-
2.2060	5890	0.1116	-	-
2.2097	5900	0.199	-	-
2.2135	5910	0.8664	-	-
2.2172	5920	0.5747	-	-
2.2210	5930	1.655	-	-
2.2247	5940	0.8386	-	-
2.2285	5950	0.5677	-	-
2.2322	5960	0.3334	-	-
2.2360	5970	0.3344	-	-
2.2397	5980	0.7777	-	-
2.2434	5990	0.359	-	-
2.2472	6000	0.8495	-	-
2.2509	6010	1.5789	-	-
2.2547	6020	0.3153	-	-
2.2584	6030	1.2525	-	-
2.2622	6040	0.9709	-	-
2.2659	6050	0.6815	-	-
2.2697	6060	0.5785	-	-
2.2734	6070	0.605	-	-
2.2772	6080	0.7768	-	-
2.2809	6090	0.2885	-	-
2.2846	6100	0.0714	-	-
2.2884	6110	1.5183	-	-
2.2921	6120	0.8578	-	-
2.2959	6130	0.4251	-	-
2.2996	6140	1.1994	-	-
2.3034	6150	0.5432	-	-
2.3071	6160	1.3702	-	-
2.3109	6170	1.3417	-	-
2.3146	6180	0.9556	-	-
2.3184	6190	0.3698	-	-
2.3221	6200	0.6896	-	-
2.3258	6210	0.6184	-	-
2.3296	6220	0.2338	-	-
2.3333	6230	0.342	-	-
2.3371	6240	0.8367	-	-
2.3408	6250	1.1024	-	-
2.3446	6260	1.0722	-	-
2.3483	6270	0.479	-	-
2.3521	6280	0.8954	-	-
2.3558	6290	0.8496	-	-
2.3596	6300	1.2729	-	-
2.3633	6310	0.1914	-	-
2.3670	6320	0.8034	-	-
2.3708	6330	0.9549	-	-
2.3745	6340	1.1065	-	-
2.3783	6350	1.5595	-	-
2.3820	6360	0.2028	-	-
2.3858	6370	0.2768	-	-
2.3895	6380	1.6269	-	-
2.3933	6390	0.8796	-	-
2.3970	6400	0.6491	-	-
2.4007	6410	0.8528	-	-
2.4045	6420	0.9181	-	-
2.4082	6430	1.2277	-	-
2.4120	6440	0.7166	-	-
2.4157	6450	1.0847	-	-
2.4195	6460	0.5212	-	-
2.4232	6470	0.0076	-	-
2.4270	6480	0.7151	-	-
2.4307	6490	0.4312	-	-
2.4345	6500	0.7043	-	-
2.4382	6510	0.3567	-	-
2.4419	6520	1.5934	-	-
2.4457	6530	0.3477	-	-
2.4494	6540	0.218	-	-
2.4532	6550	1.3359	-	-
2.4569	6560	1.4008	-	-
2.4607	6570	0.3827	-	-
2.4644	6580	0.4915	-	-
2.4682	6590	0.8245	-	-
2.4719	6600	0.7731	-	-
2.4757	6610	0.5343	-	-
2.4794	6620	0.9325	-	-
2.4831	6630	1.1319	-	-
2.4869	6640	0.3839	-	-
2.4906	6650	1.1552	-	-
2.4944	6660	1.2688	-	-
2.4981	6670	1.3912	-	-
2.5019	6680	1.1167	-	-
2.5056	6690	0.9472	-	-
2.5094	6700	0.2292	-	-
2.5131	6710	0.9339	-	-
2.5169	6720	1.5937	-	-
2.5206	6730	0.7239	-	-
2.5243	6740	0.3039	-	-
2.5281	6750	0.7881	-	-
2.5318	6760	1.1695	-	-
2.5356	6770	0.5547	-	-
2.5393	6780	1.5877	-	-
2.5431	6790	1.1356	-	-
2.5468	6800	2.0128	-	-
2.5506	6810	0.4398	-	-
2.5543	6820	0.2939	-	-
2.5581	6830	0.3846	-	-
2.5618	6840	0.5616	-	-
2.5655	6850	0.2842	-	-
2.5693	6860	0.5759	-	-
2.5730	6870	0.9994	-	-
2.5768	6880	0.7186	-	-
2.5805	6890	0.6981	-	-
2.5843	6900	0.9311	-	-
2.5880	6910	0.5467	-	-
2.5918	6920	0.4206	-	-
2.5955	6930	1.2741	-	-
2.5993	6940	0.6233	-	-
2.6030	6950	1.0366	-	-
2.6067	6960	1.3207	-	-
2.6105	6970	0.8609	-	-
2.6142	6980	0.1173	-	-
2.6180	6990	0.2675	-	-
2.6217	7000	0.223	-	-
2.6255	7010	1.3045	-	-
2.6292	7020	0.5441	-	-
2.6330	7030	0.7044	-	-
2.6367	7040	0.3714	-	-
2.6404	7050	1.1058	-	-
2.6442	7060	0.7746	-	-
2.6479	7070	0.447	-	-
2.6517	7080	0.2873	-	-
2.6554	7090	0.4244	-	-
2.6592	7100	0.1731	-	-
2.6629	7110	0.6776	-	-
2.6667	7120	0.7168	-	-
2.6704	7130	0.3992	-	-
2.6742	7140	1.2154	-	-
2.6779	7150	0.0108	-	-
2.6816	7160	0.1631	-	-
2.6854	7170	0.8925	-	-
2.6891	7180	0.4143	-	-
2.6929	7190	0.7408	-	-
2.6966	7200	0.385	-	-
2.7004	7210	0.2634	-	-
2.7041	7220	0.5994	-	-
2.7079	7230	1.6884	-	-
2.7116	7240	0.8603	-	-
2.7154	7250	1.6581	-	-
2.7191	7260	1.9287	-	-
2.7228	7270	0.7015	-	-
2.7266	7280	0.5795	-	-
2.7303	7290	0.3925	-	-
2.7341	7300	0.951	-	-
2.7378	7310	1.3277	-	-
2.7416	7320	0.8554	-	-
2.7453	7330	1.7586	-	-
2.7491	7340	0.6161	-	-
2.7528	7350	0.4676	-	-
2.7566	7360	1.1006	-	-
2.7603	7370	3.297	-	-
2.7640	7380	0.9984	-	-
2.7678	7390	0.3403	-	-
2.7715	7400	1.1086	-	-
2.7753	7410	2.0014	-	-
2.7790	7420	0.9262	-	-
2.7828	7430	0.3025	-	-
2.7865	7440	1.5384	-	-
2.7903	7450	0.3463	-	-
2.7940	7460	0.4185	-	-
2.7978	7470	0.2442	-	-
2.8015	7480	0.5406	-	-
2.8052	7490	1.0035	-	-
2.8090	7500	0.6287	-	-
2.8127	7510	0.8633	-	-
2.8165	7520	0.3335	-	-
2.8202	7530	0.5338	-	-
2.8240	7540	1.2167	-	-
2.8277	7550	0.2822	-	-
2.8315	7560	1.4419	-	-
2.8352	7570	0.4631	-	-
2.8390	7580	1.0021	-	-
2.8427	7590	0.7075	-	-
2.8464	7600	0.3762	-	-
2.8502	7610	0.6409	-	-
2.8539	7620	0.87	-	-
2.8577	7630	0.5116	-	-
2.8614	7640	0.5592	-	-
2.8652	7650	1.3226	-	-
2.8689	7660	0.7833	-	-
2.8727	7670	0.1119	-	-
2.8764	7680	0.8755	-	-
2.8801	7690	0.9209	-	-
2.8839	7700	1.042	-	-
2.8876	7710	0.261	-	-
2.8914	7720	1.2104	-	-
2.8951	7730	0.2092	-	-
2.8989	7740	0.8415	-	-
2.9026	7750	0.8367	-	-
2.9064	7760	1.4419	-	-
2.9101	7770	0.6172	-	-
2.9139	7780	0.5117	-	-
2.9176	7790	1.3995	-	-
2.9213	7800	0.3404	-	-
2.9251	7810	0.5748	-	-
2.9288	7820	0.9675	-	-
2.9326	7830	0.385	-	-
2.9363	7840	0.0263	-	-
2.9401	7850	0.6896	-	-
2.9438	7860	0.2887	-	-
2.9476	7870	1.1583	-	-
2.9513	7880	0.5359	-	-
2.9551	7890	0.1456	-	-
2.9588	7900	0.3246	-	-
2.9625	7910	1.0288	-	-
2.9663	7920	0.2144	-	-
2.9700	7930	1.2785	-	-
2.9738	7940	1.2584	-	-
2.9775	7950	0.7309	-	-
2.9813	7960	0.1397	-	-
2.9850	7970	0.2638	-	-
2.9888	7980	0.7613	-	-
2.9925	7990	0.3807	-	-
2.9963	8000	1.2176	-	-
3.0	8010	1.2772	0.9109	0.8725

Framework Versions

• Python: 3.11.9
• Sentence Transformers: 3.3.1
• Transformers: 4.48.0.dev0
• PyTorch: 2.5.1+cu124
• Accelerate: 1.2.1
• Datasets: 3.1.0
• Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

BatchHardTripletLoss

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}