File size: 5,923 Bytes
ff19902 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 | ---
base_model: cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual
tags:
- generated_from_trainer
datasets:
- all
metrics:
- precision
- recall
- f1
model-index:
- name: twitter-xlmr-clip-finetuned-all-123
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# twitter-xlmr-clip-finetuned-all-123
This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual) on the all dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7405
- Precision: 0.6431
- Recall: 0.6554
- F1: 0.6401
## 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: 16
- eval_batch_size: 16
- seed: 123
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|
| 0.6444 | 0.06 | 500 | 0.8771 | 0.6905 | 0.4537 | 0.4197 |
| 0.5499 | 0.12 | 1000 | 0.8167 | 0.7197 | 0.4260 | 0.4117 |
| 0.5357 | 0.18 | 1500 | 0.8084 | 0.7263 | 0.4696 | 0.4424 |
| 0.5175 | 0.24 | 2000 | 0.8704 | 0.6666 | 0.4266 | 0.3717 |
| 0.5285 | 0.3 | 2500 | 0.9067 | 0.7529 | 0.4565 | 0.4221 |
| 0.5081 | 0.36 | 3000 | 0.7414 | 0.7655 | 0.6114 | 0.6356 |
| 0.506 | 0.42 | 3500 | 0.8713 | 0.5830 | 0.6591 | 0.5786 |
| 0.5049 | 0.48 | 4000 | 0.7514 | 0.5551 | 0.4568 | 0.4464 |
| 0.4999 | 0.54 | 4500 | 0.7584 | 0.6519 | 0.5502 | 0.5767 |
| 0.507 | 0.6 | 5000 | 0.8072 | 0.6479 | 0.5626 | 0.5636 |
| 0.5048 | 0.66 | 5500 | 0.8080 | 0.6260 | 0.5725 | 0.5730 |
| 0.4907 | 0.72 | 6000 | 0.7966 | 0.6976 | 0.5138 | 0.5224 |
| 0.493 | 0.78 | 6500 | 0.8193 | 0.7099 | 0.4949 | 0.4922 |
| 0.4668 | 0.84 | 7000 | 0.7502 | 0.6282 | 0.6942 | 0.6501 |
| 0.4717 | 0.9 | 7500 | 0.7636 | 0.6372 | 0.5109 | 0.5191 |
| 0.4774 | 0.96 | 8000 | 0.7652 | 0.7513 | 0.5360 | 0.5587 |
| 0.4676 | 1.02 | 8500 | 0.8482 | 0.6372 | 0.5918 | 0.5836 |
| 0.4361 | 1.08 | 9000 | 0.7456 | 0.6687 | 0.5177 | 0.5175 |
| 0.4536 | 1.14 | 9500 | 0.8449 | 0.7363 | 0.5160 | 0.5156 |
| 0.4277 | 1.2 | 10000 | 0.8648 | 0.6382 | 0.5247 | 0.5173 |
| 0.4444 | 1.26 | 10500 | 0.8723 | 0.5871 | 0.6622 | 0.5959 |
| 0.4269 | 1.32 | 11000 | 0.7856 | 0.6151 | 0.5521 | 0.5526 |
| 0.4322 | 1.38 | 11500 | 0.7405 | 0.6431 | 0.6554 | 0.6401 |
| 0.4435 | 1.44 | 12000 | 0.7682 | 0.6568 | 0.5751 | 0.5923 |
| 0.4429 | 1.5 | 12500 | 0.8824 | 0.5956 | 0.6006 | 0.5545 |
| 0.4381 | 1.56 | 13000 | 0.7879 | 0.4457 | 0.4727 | 0.4395 |
| 0.4389 | 1.62 | 13500 | 0.7555 | 0.6260 | 0.6984 | 0.6502 |
| 0.4529 | 1.68 | 14000 | 0.7981 | 0.6621 | 0.5546 | 0.5663 |
| 0.4509 | 1.74 | 14500 | 0.7827 | 0.6160 | 0.6321 | 0.6172 |
| 0.4413 | 1.8 | 15000 | 0.7895 | 0.6381 | 0.6357 | 0.6285 |
| 0.4198 | 1.86 | 15500 | 0.8345 | 0.5940 | 0.5526 | 0.5602 |
| 0.4415 | 1.92 | 16000 | 0.8746 | 0.6615 | 0.6612 | 0.6459 |
| 0.443 | 1.98 | 16500 | 0.8155 | 0.6516 | 0.5265 | 0.5352 |
| 0.4068 | 2.04 | 17000 | 0.7642 | 0.5838 | 0.6220 | 0.5975 |
| 0.3905 | 2.1 | 17500 | 0.7929 | 0.6720 | 0.5555 | 0.5740 |
| 0.3969 | 2.16 | 18000 | 0.8949 | 0.5330 | 0.4771 | 0.4687 |
| 0.3841 | 2.22 | 18500 | 0.9233 | 0.6028 | 0.5410 | 0.5492 |
| 0.4031 | 2.28 | 19000 | 0.7720 | 0.6089 | 0.5719 | 0.5776 |
| 0.3878 | 2.34 | 19500 | 0.9046 | 0.6265 | 0.5358 | 0.5318 |
| 0.4001 | 2.41 | 20000 | 0.8451 | 0.6960 | 0.5622 | 0.5761 |
| 0.3997 | 2.47 | 20500 | 0.8964 | 0.6170 | 0.5665 | 0.5541 |
| 0.3945 | 2.53 | 21000 | 0.8001 | 0.5553 | 0.5180 | 0.5195 |
| 0.4005 | 2.59 | 21500 | 0.8357 | 0.5519 | 0.5100 | 0.5170 |
| 0.3907 | 2.65 | 22000 | 0.8017 | 0.5884 | 0.5409 | 0.5552 |
| 0.3858 | 2.71 | 22500 | 0.8283 | 0.6036 | 0.5792 | 0.5862 |
| 0.3973 | 2.77 | 23000 | 0.9024 | 0.5770 | 0.5665 | 0.5393 |
| 0.3969 | 2.83 | 23500 | 0.8341 | 0.5642 | 0.5528 | 0.5558 |
| 0.3911 | 2.89 | 24000 | 0.8966 | 0.6045 | 0.5088 | 0.5070 |
| 0.3856 | 2.95 | 24500 | 0.8349 | 0.6021 | 0.5586 | 0.5689 |
| 0.3961 | 3.01 | 25000 | 0.9364 | 0.6119 | 0.5412 | 0.5585 |
| 0.3301 | 3.07 | 25500 | 0.9542 | 0.5757 | 0.6084 | 0.5813 |
| 0.3385 | 3.13 | 26000 | 1.0137 | 0.5563 | 0.5294 | 0.5346 |
| 0.3475 | 3.19 | 26500 | 0.9311 | 0.6359 | 0.5675 | 0.5822 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|