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xlm-roberta-base-NER-favsbot

Token Classification
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PyTorch
xlm-roberta
generated_from_trainer
Eval Results
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update model card README.md

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@@ -24,16 +24,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.48
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  - name: Recall
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  type: recall
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- value: 0.13333333333333333
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  - name: F1
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  type: f1
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- value: 0.20869565217391306
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  - name: Accuracy
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  type: accuracy
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- value: 0.504524886877828
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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
@@ -43,11 +43,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the favsbot dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.4830
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- - Precision: 0.48
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- - Recall: 0.1333
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- - F1: 0.2087
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- - Accuracy: 0.5045
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  ## Model description
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@@ -72,22 +72,32 @@ The following hyperparameters were used during training:
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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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- - num_epochs: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 1.0 | 4 | 2.4132 | 0.0498 | 0.1222 | 0.0707 | 0.0543 |
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- | No log | 2.0 | 8 | 2.2414 | 0.0769 | 0.0944 | 0.0848 | 0.3371 |
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- | No log | 3.0 | 12 | 2.0113 | 0.0 | 0.0 | 0.0 | 0.4321 |
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- | No log | 4.0 | 16 | 1.7285 | 0.0 | 0.0 | 0.0 | 0.4321 |
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- | No log | 5.0 | 20 | 1.6857 | 0.0 | 0.0 | 0.0 | 0.4321 |
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- | No log | 6.0 | 24 | 1.6241 | 0.7 | 0.0389 | 0.0737 | 0.4502 |
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- | No log | 7.0 | 28 | 1.5622 | 0.5143 | 0.1 | 0.1674 | 0.4864 |
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- | No log | 8.0 | 32 | 1.5195 | 0.4808 | 0.1389 | 0.2155 | 0.5068 |
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- | No log | 9.0 | 36 | 1.4924 | 0.4898 | 0.1333 | 0.2096 | 0.5045 |
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- | No log | 10.0 | 40 | 1.4830 | 0.48 | 0.1333 | 0.2087 | 0.5045 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.5555555555555556
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  - name: Recall
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  type: recall
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+ value: 0.4722222222222222
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  - name: F1
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  type: f1
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+ value: 0.5105105105105106
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  - name: Accuracy
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  type: accuracy
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+ value: 0.6900452488687783
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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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  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the favsbot dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.0572
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+ - Precision: 0.5556
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+ - Recall: 0.4722
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+ - F1: 0.5105
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+ - Accuracy: 0.6900
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  ## Model description
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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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+ - num_epochs: 20
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 4 | 2.4303 | 0.1448 | 0.3556 | 0.2058 | 0.1855 |
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+ | No log | 2.0 | 8 | 2.3220 | 0.1465 | 0.3556 | 0.2075 | 0.1991 |
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+ | No log | 3.0 | 12 | 2.1842 | 0.2486 | 0.2389 | 0.2436 | 0.4593 |
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+ | No log | 4.0 | 16 | 1.9552 | 0.4 | 0.0111 | 0.0216 | 0.4367 |
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+ | No log | 5.0 | 20 | 1.6989 | 0.0 | 0.0 | 0.0 | 0.4321 |
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+ | No log | 6.0 | 24 | 1.6532 | 0.5 | 0.0056 | 0.0110 | 0.4344 |
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+ | No log | 7.0 | 28 | 1.5724 | 0.3649 | 0.15 | 0.2126 | 0.5045 |
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+ | No log | 8.0 | 32 | 1.5164 | 0.3654 | 0.2111 | 0.2676 | 0.5271 |
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+ | No log | 9.0 | 36 | 1.4448 | 0.4203 | 0.1611 | 0.2329 | 0.5090 |
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+ | No log | 10.0 | 40 | 1.3922 | 0.4833 | 0.1611 | 0.2417 | 0.5158 |
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+ | No log | 11.0 | 44 | 1.3409 | 0.5395 | 0.2278 | 0.3203 | 0.5498 |
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+ | No log | 12.0 | 48 | 1.2831 | 0.5824 | 0.2944 | 0.3911 | 0.5950 |
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+ | No log | 13.0 | 52 | 1.2269 | 0.5714 | 0.3556 | 0.4384 | 0.6335 |
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+ | No log | 14.0 | 56 | 1.1766 | 0.5625 | 0.4 | 0.4675 | 0.6606 |
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+ | No log | 15.0 | 60 | 1.1408 | 0.5540 | 0.4278 | 0.4828 | 0.6674 |
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+ | No log | 16.0 | 64 | 1.1159 | 0.56 | 0.4667 | 0.5091 | 0.6810 |
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+ | No log | 17.0 | 68 | 1.0908 | 0.5658 | 0.4778 | 0.5181 | 0.6855 |
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+ | No log | 18.0 | 72 | 1.0722 | 0.5658 | 0.4778 | 0.5181 | 0.6923 |
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+ | No log | 19.0 | 76 | 1.0615 | 0.5592 | 0.4722 | 0.5120 | 0.6900 |
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+ | No log | 20.0 | 80 | 1.0572 | 0.5556 | 0.4722 | 0.5105 | 0.6900 |
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  ### Framework versions