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asquevedos
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services-ucacue

Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
Inference Endpoints
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  2. model.safetensors +1 -1
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: BSC-LT/roberta-base-bne
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: services-ucacue
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # services-ucacue
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+
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+ This model is a fine-tuned version of [BSC-LT/roberta-base-bne](https://huggingface.co/BSC-LT/roberta-base-bne) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.2478
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+ - Accuracy: 0.8352
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 40
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+ - eval_batch_size: 48
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 500
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+ - num_epochs: 20
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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+ | 1.3181 | 0.16 | 100 | 0.9069 | 0.6518 |
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+ | 0.7432 | 0.32 | 200 | 0.6677 | 0.7551 |
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+ | 0.6287 | 0.47 | 300 | 0.5875 | 0.7858 |
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+ | 0.5838 | 0.63 | 400 | 0.5399 | 0.7963 |
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+ | 0.5493 | 0.79 | 500 | 0.5858 | 0.7871 |
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+ | 0.517 | 0.95 | 600 | 0.5136 | 0.8102 |
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+ | 0.4556 | 1.11 | 700 | 0.5451 | 0.7950 |
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+ | 0.4213 | 1.27 | 800 | 0.5288 | 0.7969 |
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+ | 0.4168 | 1.42 | 900 | 0.4665 | 0.8267 |
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+ | 0.4234 | 1.58 | 1000 | 0.4680 | 0.8346 |
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+ | 0.4202 | 1.74 | 1100 | 0.4615 | 0.8327 |
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+ | 0.4343 | 1.9 | 1200 | 0.4756 | 0.8251 |
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+ | 0.3699 | 2.06 | 1300 | 0.5059 | 0.8403 |
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+ | 0.2934 | 2.22 | 1400 | 0.4621 | 0.8321 |
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+ | 0.3074 | 2.37 | 1500 | 0.5008 | 0.8394 |
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+ | 0.3213 | 2.53 | 1600 | 0.4685 | 0.8343 |
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+ | 0.309 | 2.69 | 1700 | 0.4761 | 0.8390 |
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+ | 0.2922 | 2.85 | 1800 | 0.4530 | 0.8387 |
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+ | 0.2996 | 3.01 | 1900 | 0.5078 | 0.8352 |
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+ | 0.1917 | 3.16 | 2000 | 0.6382 | 0.8248 |
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+ | 0.1817 | 3.32 | 2100 | 0.5286 | 0.8305 |
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+ | 0.2172 | 3.48 | 2200 | 0.5374 | 0.8356 |
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+ | 0.225 | 3.64 | 2300 | 0.5987 | 0.8226 |
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+ | 0.2306 | 3.8 | 2400 | 0.5182 | 0.8447 |
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+ | 0.2348 | 3.96 | 2500 | 0.5315 | 0.8346 |
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+ | 0.1636 | 4.11 | 2600 | 0.6174 | 0.8295 |
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+ | 0.145 | 4.27 | 2700 | 0.5829 | 0.8330 |
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+ | 0.159 | 4.43 | 2800 | 0.6558 | 0.8352 |
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+ | 0.1546 | 4.59 | 2900 | 0.5983 | 0.8279 |
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+ | 0.1674 | 4.75 | 3000 | 0.5318 | 0.8349 |
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+ | 0.1667 | 4.91 | 3100 | 0.6102 | 0.8330 |
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+ | 0.1553 | 5.06 | 3200 | 0.7027 | 0.8264 |
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+ | 0.1047 | 5.22 | 3300 | 0.8185 | 0.8324 |
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+ | 0.1294 | 5.38 | 3400 | 0.7657 | 0.8349 |
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+ | 0.1287 | 5.54 | 3500 | 0.7114 | 0.8340 |
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+ | 0.1403 | 5.7 | 3600 | 0.6230 | 0.8321 |
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+ | 0.1358 | 5.85 | 3700 | 0.6789 | 0.8349 |
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+ | 0.119 | 6.01 | 3800 | 0.6755 | 0.8435 |
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+ | 0.0812 | 6.17 | 3900 | 0.8343 | 0.8305 |
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+ | 0.0977 | 6.33 | 4000 | 0.8252 | 0.8251 |
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+ | 0.1036 | 6.49 | 4100 | 0.8672 | 0.8298 |
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+ | 0.1011 | 6.65 | 4200 | 0.8164 | 0.8245 |
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+ | 0.1303 | 6.8 | 4300 | 0.7829 | 0.8311 |
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+ | 0.121 | 6.96 | 4400 | 0.6958 | 0.8343 |
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+ | 0.0797 | 7.12 | 4500 | 0.9208 | 0.8394 |
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+ | 0.0832 | 7.28 | 4600 | 0.8302 | 0.8352 |
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+ | 0.0869 | 7.44 | 4700 | 0.9605 | 0.8333 |
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+ | 0.0825 | 7.59 | 4800 | 0.9242 | 0.8295 |
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+ | 0.1019 | 7.75 | 4900 | 0.8342 | 0.8337 |
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+ | 0.1081 | 7.91 | 5000 | 0.8462 | 0.8305 |
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+ | 0.1016 | 8.07 | 5100 | 0.8536 | 0.8257 |
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+ | 0.078 | 8.23 | 5200 | 0.9047 | 0.8298 |
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+ | 0.0778 | 8.39 | 5300 | 0.9631 | 0.8292 |
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+ | 0.0723 | 8.54 | 5400 | 0.9283 | 0.8327 |
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+ | 0.0875 | 8.7 | 5500 | 0.9040 | 0.8305 |
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+ | 0.0899 | 8.86 | 5600 | 0.8884 | 0.8305 |
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+ | 0.0803 | 9.02 | 5700 | 0.9168 | 0.8321 |
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+ | 0.0549 | 9.18 | 5800 | 1.0361 | 0.8378 |
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+ | 0.0697 | 9.34 | 5900 | 1.0312 | 0.8413 |
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+ | 0.0714 | 9.49 | 6000 | 0.9170 | 0.8381 |
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+ | 0.0789 | 9.65 | 6100 | 0.8447 | 0.8352 |
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+ | 0.0673 | 9.81 | 6200 | 0.8850 | 0.8327 |
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+ | 0.0773 | 9.97 | 6300 | 0.9276 | 0.8403 |
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+ | 0.0577 | 10.13 | 6400 | 0.8892 | 0.8368 |
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+ | 0.0517 | 10.28 | 6500 | 1.0524 | 0.8264 |
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+ | 0.0551 | 10.44 | 6600 | 0.9936 | 0.8260 |
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+ | 0.0532 | 10.6 | 6700 | 1.1169 | 0.8321 |
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+ | 0.0726 | 10.76 | 6800 | 1.0498 | 0.8273 |
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+ | 0.0608 | 10.92 | 6900 | 0.9969 | 0.8343 |
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+ | 0.0598 | 11.08 | 7000 | 1.0024 | 0.8371 |
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+ | 0.0502 | 11.23 | 7100 | 1.0547 | 0.8251 |
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+ | 0.0615 | 11.39 | 7200 | 0.9235 | 0.8298 |
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+ | 0.0545 | 11.55 | 7300 | 0.9389 | 0.8362 |
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+ | 0.0565 | 11.71 | 7400 | 0.8622 | 0.8390 |
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+ | 0.0601 | 11.87 | 7500 | 0.9792 | 0.8381 |
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+ | 0.0623 | 12.03 | 7600 | 1.0572 | 0.8359 |
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+ | 0.0494 | 12.18 | 7700 | 1.0454 | 0.8394 |
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+ | 0.0561 | 12.34 | 7800 | 1.0160 | 0.8390 |
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+ | 0.0459 | 12.5 | 7900 | 1.0492 | 0.8384 |
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+ | 0.0539 | 12.66 | 8000 | 0.9913 | 0.8413 |
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+ | 0.052 | 12.82 | 8100 | 0.9678 | 0.8394 |
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+ | 0.0524 | 12.97 | 8200 | 0.9991 | 0.8359 |
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+ | 0.0476 | 13.13 | 8300 | 0.9980 | 0.8359 |
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+ | 0.0384 | 13.29 | 8400 | 1.0535 | 0.8365 |
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+ | 0.0484 | 13.45 | 8500 | 1.0327 | 0.8416 |
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+ | 0.0461 | 13.61 | 8600 | 1.0804 | 0.8406 |
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+ | 0.056 | 13.77 | 8700 | 1.0189 | 0.8359 |
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+ | 0.0499 | 13.92 | 8800 | 1.0734 | 0.8349 |
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+ | 0.0463 | 14.08 | 8900 | 1.0612 | 0.8343 |
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+ | 0.0409 | 14.24 | 9000 | 1.1206 | 0.8321 |
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+ | 0.043 | 14.4 | 9100 | 1.0902 | 0.8368 |
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+ | 0.0391 | 14.56 | 9200 | 1.0407 | 0.8340 |
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+ | 0.0438 | 14.72 | 9300 | 1.0803 | 0.8352 |
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+ | 0.0404 | 14.87 | 9400 | 1.0797 | 0.8362 |
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+ | 0.0514 | 15.03 | 9500 | 1.1111 | 0.8365 |
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+ | 0.0341 | 15.19 | 9600 | 1.1324 | 0.8337 |
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+ | 0.0399 | 15.35 | 9700 | 1.1461 | 0.8375 |
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+ | 0.0486 | 15.51 | 9800 | 1.0840 | 0.8375 |
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+ | 0.0396 | 15.66 | 9900 | 1.1105 | 0.8340 |
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+ | 0.0411 | 15.82 | 10000 | 1.0873 | 0.8362 |
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+ | 0.0391 | 15.98 | 10100 | 1.1769 | 0.8333 |
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+ | 0.0419 | 16.14 | 10200 | 1.1856 | 0.8324 |
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+ | 0.0371 | 16.3 | 10300 | 1.2263 | 0.8292 |
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+ | 0.0361 | 16.46 | 10400 | 1.2021 | 0.8333 |
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+ | 0.0374 | 16.61 | 10500 | 1.2242 | 0.8292 |
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+ | 0.0383 | 16.77 | 10600 | 1.1600 | 0.8384 |
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+ | 0.035 | 16.93 | 10700 | 1.1955 | 0.8356 |
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+ | 0.0378 | 17.09 | 10800 | 1.1868 | 0.8340 |
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+ | 0.0372 | 17.25 | 10900 | 1.2195 | 0.8302 |
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+ | 0.037 | 17.41 | 11000 | 1.2149 | 0.8324 |
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+ | 0.0342 | 17.56 | 11100 | 1.2127 | 0.8337 |
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+ | 0.035 | 17.72 | 11200 | 1.2074 | 0.8362 |
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+ | 0.0405 | 17.88 | 11300 | 1.2263 | 0.8327 |
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+ | 0.0343 | 18.04 | 11400 | 1.2197 | 0.8333 |
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+ | 0.0349 | 18.2 | 11500 | 1.2334 | 0.8337 |
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+ | 0.0378 | 18.35 | 11600 | 1.2108 | 0.8365 |
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+ | 0.0298 | 18.51 | 11700 | 1.2167 | 0.8356 |
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+ | 0.0404 | 18.67 | 11800 | 1.2331 | 0.8371 |
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+ | 0.0342 | 18.83 | 11900 | 1.2202 | 0.8337 |
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+ | 0.0331 | 18.99 | 12000 | 1.2222 | 0.8346 |
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+ | 0.032 | 19.15 | 12100 | 1.2287 | 0.8337 |
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+ | 0.0299 | 19.3 | 12200 | 1.2368 | 0.8333 |
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+ | 0.0332 | 19.46 | 12300 | 1.2439 | 0.8352 |
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+ | 0.0353 | 19.62 | 12400 | 1.2481 | 0.8359 |
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+ | 0.0353 | 19.78 | 12500 | 1.2485 | 0.8349 |
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+ | 0.0304 | 19.94 | 12600 | 1.2478 | 0.8352 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.39.3
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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