transformers pytorch xlm-roberta text-classification generated_from_trainer dataset:massive base_model:facebook/xlm-v-base base_model:finetune:facebook/xlm-v-base license:mit model-index autotrain_compatible endpoints_compatible region:us
---
base_model: facebook/xlm-v-base
datasets:
- massive
license: mit
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: scenario-TCR_data-en-massive_all_1_1
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: massive
      type: massive
      config: all_1.1
      split: validation
      args: all_1.1
    metrics:
    - type: accuracy
      value: 0.7100778333960244
      name: Accuracy
    - type: f1
      value: 0.6550778448597152
      name: F1
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# scenario-TCR_data-en-massive_all_1_1

This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3802
- Accuracy: 0.7101
- F1: 0.6551

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| No log        | 0.28  | 100   | 3.6542          | 0.0800   | 0.0085 |
| No log        | 0.56  | 200   | 2.9766          | 0.3048   | 0.0953 |
| No log        | 0.83  | 300   | 2.4835          | 0.3498   | 0.1168 |
| No log        | 1.11  | 400   | 2.1305          | 0.4616   | 0.2154 |
| 2.7657        | 1.39  | 500   | 1.8889          | 0.5374   | 0.2791 |
| 2.7657        | 1.67  | 600   | 1.7326          | 0.5726   | 0.3208 |
| 2.7657        | 1.94  | 700   | 1.6536          | 0.5870   | 0.3726 |
| 2.7657        | 2.22  | 800   | 1.6709          | 0.5987   | 0.4014 |
| 2.7657        | 2.5   | 900   | 1.5460          | 0.6337   | 0.4720 |
| 1.1591        | 2.78  | 1000  | 1.5165          | 0.6434   | 0.4904 |
| 1.1591        | 3.06  | 1100  | 1.3861          | 0.6736   | 0.5237 |
| 1.1591        | 3.33  | 1200  | 1.3776          | 0.6739   | 0.5320 |
| 1.1591        | 3.61  | 1300  | 1.3753          | 0.6734   | 0.5521 |
| 1.1591        | 3.89  | 1400  | 1.4680          | 0.6624   | 0.5368 |
| 0.6194        | 4.17  | 1500  | 1.3899          | 0.6795   | 0.5520 |
| 0.6194        | 4.44  | 1600  | 1.5509          | 0.6640   | 0.5482 |
| 0.6194        | 4.72  | 1700  | 1.4034          | 0.6837   | 0.5764 |
| 0.6194        | 5.0   | 1800  | 1.4750          | 0.6739   | 0.5814 |
| 0.6194        | 5.28  | 1900  | 1.5321          | 0.6697   | 0.5761 |
| 0.3858        | 5.56  | 2000  | 1.5022          | 0.6822   | 0.5912 |
| 0.3858        | 5.83  | 2100  | 1.4612          | 0.6865   | 0.6016 |
| 0.3858        | 6.11  | 2200  | 1.4079          | 0.7034   | 0.6204 |
| 0.3858        | 6.39  | 2300  | 1.5165          | 0.6922   | 0.6296 |
| 0.3858        | 6.67  | 2400  | 1.6168          | 0.6736   | 0.6157 |
| 0.259         | 6.94  | 2500  | 1.5425          | 0.6948   | 0.6261 |
| 0.259         | 7.22  | 2600  | 1.6145          | 0.6796   | 0.6035 |
| 0.259         | 7.5   | 2700  | 1.5916          | 0.6824   | 0.6175 |
| 0.259         | 7.78  | 2800  | 1.5966          | 0.6977   | 0.6306 |
| 0.259         | 8.06  | 2900  | 1.4939          | 0.7125   | 0.6274 |
| 0.1759        | 8.33  | 3000  | 1.8425          | 0.6714   | 0.6170 |
| 0.1759        | 8.61  | 3100  | 1.6688          | 0.6923   | 0.6403 |
| 0.1759        | 8.89  | 3200  | 1.6218          | 0.6997   | 0.6220 |
| 0.1759        | 9.17  | 3300  | 1.7825          | 0.6829   | 0.6223 |
| 0.1759        | 9.44  | 3400  | 1.8706          | 0.6916   | 0.6294 |
| 0.1162        | 9.72  | 3500  | 1.8082          | 0.6884   | 0.6280 |
| 0.1162        | 10.0  | 3600  | 1.6708          | 0.7096   | 0.6338 |
| 0.1162        | 10.28 | 3700  | 1.7170          | 0.7100   | 0.6490 |
| 0.1162        | 10.56 | 3800  | 1.8575          | 0.6917   | 0.6264 |
| 0.1162        | 10.83 | 3900  | 1.8307          | 0.6959   | 0.6448 |
| 0.092         | 11.11 | 4000  | 1.9248          | 0.6958   | 0.6359 |
| 0.092         | 11.39 | 4100  | 1.7551          | 0.7162   | 0.6508 |
| 0.092         | 11.67 | 4200  | 1.8234          | 0.7072   | 0.6465 |
| 0.092         | 11.94 | 4300  | 2.1146          | 0.6790   | 0.6285 |
| 0.092         | 12.22 | 4400  | 1.9964          | 0.6909   | 0.6411 |
| 0.0582        | 12.5  | 4500  | 2.0290          | 0.6852   | 0.6313 |
| 0.0582        | 12.78 | 4600  | 2.0828          | 0.6838   | 0.6355 |
| 0.0582        | 13.06 | 4700  | 1.9272          | 0.7013   | 0.6312 |
| 0.0582        | 13.33 | 4800  | 1.9882          | 0.6959   | 0.6334 |
| 0.0582        | 13.61 | 4900  | 1.9552          | 0.7116   | 0.6511 |
| 0.0398        | 13.89 | 5000  | 2.0269          | 0.7060   | 0.6451 |
| 0.0398        | 14.17 | 5100  | 2.1377          | 0.6929   | 0.6414 |
| 0.0398        | 14.44 | 5200  | 2.1114          | 0.6880   | 0.6373 |
| 0.0398        | 14.72 | 5300  | 2.1517          | 0.6927   | 0.6438 |
| 0.0398        | 15.0  | 5400  | 2.2472          | 0.6921   | 0.6499 |
| 0.0311        | 15.28 | 5500  | 2.1801          | 0.6993   | 0.6557 |
| 0.0311        | 15.56 | 5600  | 2.1090          | 0.7020   | 0.6458 |
| 0.0311        | 15.83 | 5700  | 2.0049          | 0.7160   | 0.6590 |
| 0.0311        | 16.11 | 5800  | 2.2198          | 0.6959   | 0.6460 |
| 0.0311        | 16.39 | 5900  | 2.1074          | 0.7087   | 0.6519 |
| 0.0223        | 16.67 | 6000  | 2.0899          | 0.7096   | 0.6563 |
| 0.0223        | 16.94 | 6100  | 2.1736          | 0.7026   | 0.6546 |
| 0.0223        | 17.22 | 6200  | 2.1829          | 0.7004   | 0.6496 |
| 0.0223        | 17.5  | 6300  | 2.2041          | 0.6973   | 0.6450 |
| 0.0223        | 17.78 | 6400  | 2.1969          | 0.7074   | 0.6566 |
| 0.0178        | 18.06 | 6500  | 2.4021          | 0.6931   | 0.6515 |
| 0.0178        | 18.33 | 6600  | 2.2865          | 0.7092   | 0.6619 |
| 0.0178        | 18.61 | 6700  | 2.3086          | 0.7018   | 0.6504 |
| 0.0178        | 18.89 | 6800  | 2.2665          | 0.7054   | 0.6535 |
| 0.0178        | 19.17 | 6900  | 2.2723          | 0.7061   | 0.6525 |
| 0.0129        | 19.44 | 7000  | 2.2976          | 0.7030   | 0.6483 |
| 0.0129        | 19.72 | 7100  | 2.3634          | 0.7011   | 0.6514 |
| 0.0129        | 20.0  | 7200  | 2.3313          | 0.6971   | 0.6464 |
| 0.0129        | 20.28 | 7300  | 2.4373          | 0.6907   | 0.6439 |
| 0.0129        | 20.56 | 7400  | 2.2424          | 0.7139   | 0.6588 |
| 0.0125        | 20.83 | 7500  | 2.2329          | 0.7098   | 0.6547 |
| 0.0125        | 21.11 | 7600  | 2.2365          | 0.7107   | 0.6607 |
| 0.0125        | 21.39 | 7700  | 2.2925          | 0.7096   | 0.6593 |
| 0.0125        | 21.67 | 7800  | 2.3717          | 0.6998   | 0.6486 |
| 0.0125        | 21.94 | 7900  | 2.4211          | 0.6951   | 0.6479 |
| 0.0104        | 22.22 | 8000  | 2.3714          | 0.6978   | 0.6434 |
| 0.0104        | 22.5  | 8100  | 2.3995          | 0.7004   | 0.6503 |
| 0.0104        | 22.78 | 8200  | 2.3877          | 0.7044   | 0.6521 |
| 0.0104        | 23.06 | 8300  | 2.4957          | 0.6972   | 0.6482 |
| 0.0104        | 23.33 | 8400  | 2.2553          | 0.7180   | 0.6591 |
| 0.0061        | 23.61 | 8500  | 2.3877          | 0.7068   | 0.6560 |
| 0.0061        | 23.89 | 8600  | 2.4298          | 0.7036   | 0.6557 |
| 0.0061        | 24.17 | 8700  | 2.3903          | 0.7055   | 0.6516 |
| 0.0061        | 24.44 | 8800  | 2.3298          | 0.7065   | 0.6493 |
| 0.0061        | 24.72 | 8900  | 2.3245          | 0.7110   | 0.6535 |
| 0.0054        | 25.0  | 9000  | 2.3287          | 0.7086   | 0.6494 |
| 0.0054        | 25.28 | 9100  | 2.4519          | 0.6989   | 0.6427 |
| 0.0054        | 25.56 | 9200  | 2.4671          | 0.6988   | 0.6421 |
| 0.0054        | 25.83 | 9300  | 2.5166          | 0.6955   | 0.6447 |
| 0.0054        | 26.11 | 9400  | 2.4190          | 0.7056   | 0.6500 |
| 0.0029        | 26.39 | 9500  | 2.4361          | 0.7049   | 0.6511 |
| 0.0029        | 26.67 | 9600  | 2.4765          | 0.7029   | 0.6496 |
| 0.0029        | 26.94 | 9700  | 2.5246          | 0.6988   | 0.6460 |
| 0.0029        | 27.22 | 9800  | 2.4363          | 0.7051   | 0.6491 |
| 0.0029        | 27.5  | 9900  | 2.4066          | 0.7075   | 0.6514 |
| 0.0025        | 27.78 | 10000 | 2.3870          | 0.7092   | 0.6556 |
| 0.0025        | 28.06 | 10100 | 2.4028          | 0.7081   | 0.6539 |
| 0.0025        | 28.33 | 10200 | 2.3983          | 0.7080   | 0.6537 |
| 0.0025        | 28.61 | 10300 | 2.3876          | 0.7088   | 0.6552 |
| 0.0025        | 28.89 | 10400 | 2.4032          | 0.7080   | 0.6542 |
| 0.0025        | 29.17 | 10500 | 2.4138          | 0.7081   | 0.6544 |
| 0.0025        | 29.44 | 10600 | 2.3880          | 0.7098   | 0.6555 |
| 0.0025        | 29.72 | 10700 | 2.3801          | 0.7100   | 0.6552 |
| 0.0025        | 30.0  | 10800 | 2.3802          | 0.7101   | 0.6551 |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
