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bge-base-en-v1.5-2024-12-05_13-34-00

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metadata
library_name: transformers
license: mit
base_model: BAAI/bge-base-en-v1.5
tags:
  - generated_from_trainer
model-index:
  - name: bge-base-en-v1.5-2024-12-05_13-34-00
    results: []

bge-base-en-v1.5-2024-12-05_13-34-00

This model is a fine-tuned version of BAAI/bge-base-en-v1.5 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0228
  • Spearman: 0.9285
  • Pearson: 0.9285
  • Mse: 0.0228

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.05
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Spearman Pearson Mse
0.0525 0.1000 2110 0.0402 0.8683 0.8725 0.0402
0.0351 0.1999 4220 0.0424 0.8824 0.8816 0.0424
0.0314 0.2999 6330 0.0355 0.8829 0.8860 0.0355
0.038 0.3999 8440 0.0339 0.8940 0.8932 0.0339
0.0287 0.4998 10550 0.0318 0.8959 0.8982 0.0318
0.027 0.5998 12660 0.0308 0.8986 0.9010 0.0308
0.0376 0.6998 14770 0.0309 0.9019 0.9038 0.0309
0.0264 0.7997 16880 0.0301 0.9017 0.9049 0.0301
0.0311 0.8997 18990 0.0294 0.9030 0.9070 0.0294
0.0303 0.9997 21100 0.0286 0.9052 0.9085 0.0286
0.0252 1.0996 23210 0.0290 0.9085 0.9093 0.0290
0.0312 1.1996 25320 0.0287 0.9084 0.9093 0.0287
0.0238 1.2996 27430 0.0277 0.9095 0.9132 0.0277
0.0326 1.3995 29540 0.0295 0.9089 0.9096 0.0295
0.0204 1.4995 31650 0.0272 0.9104 0.9132 0.0272
0.0237 1.5995 33760 0.0304 0.9120 0.9099 0.0304
0.0285 1.6994 35870 0.0263 0.9128 0.9168 0.0263
0.0218 1.7994 37980 0.0262 0.9152 0.9185 0.0262
0.032 1.8994 40090 0.0259 0.9149 0.9186 0.0259
0.0211 1.9993 42200 0.0256 0.9155 0.9197 0.0256
0.0209 2.0993 44310 0.0253 0.9174 0.9190 0.0253
0.016 2.1993 46420 0.0259 0.9180 0.9194 0.0259
0.0122 2.2992 48530 0.0257 0.9181 0.9211 0.0257
0.0147 2.3992 50640 0.0276 0.9205 0.9210 0.0276
0.015 2.4992 52750 0.0253 0.9196 0.9223 0.0253
0.0201 2.5991 54860 0.0243 0.9208 0.9238 0.0243
0.0137 2.6991 56970 0.0243 0.9214 0.9232 0.0243
0.0158 2.7991 59080 0.0239 0.9224 0.9250 0.0239
0.018 2.8990 61190 0.0238 0.9234 0.9258 0.0238
0.0175 2.9990 63300 0.0234 0.9231 0.9264 0.0234
0.0122 3.0990 65410 0.0234 0.9241 0.9265 0.0234
0.0107 3.1989 67520 0.0238 0.9241 0.9264 0.0238
0.0081 3.2989 69630 0.0238 0.9248 0.9264 0.0238
0.0093 3.3989 71740 0.0233 0.9251 0.9270 0.0233
0.0128 3.4988 73850 0.0229 0.9258 0.9279 0.0229
0.0106 3.5988 75960 0.0231 0.9260 0.9281 0.0231
0.0134 3.6988 78070 0.0230 0.9261 0.9284 0.0230
0.0087 3.7987 80180 0.0227 0.9269 0.9295 0.0227
0.0086 3.8987 82290 0.0228 0.9267 0.9290 0.0228
0.0101 3.9987 84400 0.0225 0.9271 0.9294 0.0225
0.0075 4.0986 86510 0.0227 0.9271 0.9290 0.0227
0.0086 4.1986 88620 0.0225 0.9273 0.9295 0.0225
0.0067 4.2986 90730 0.0227 0.9277 0.9293 0.0227
0.009 4.3985 92840 0.0224 0.9275 0.9297 0.0224
0.007 4.4985 94950 0.0225 0.9278 0.9297 0.0225
0.0066 4.5985 97060 0.0225 0.9280 0.9300 0.0225
0.0118 4.6984 99170 0.0225 0.9279 0.9298 0.0225
0.0071 4.7984 101280 0.0225 0.9279 0.9299 0.0225
0.0071 4.8984 103390 0.0225 0.9280 0.9299 0.0225
0.0087 4.9983 105500 0.0225 0.9280 0.9299 0.0225

Framework versions

  • Transformers 4.46.3
  • Pytorch 2.4.1+cu121
  • Datasets 2.19.2
  • Tokenizers 0.20.3