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Dagobert42
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xlnet-base-cased-biored-augmented

Token Classification
Transformers
Safetensors
English
xlnet
low-resource NER
token_classification
biomedicine
medical NER
Generated from Trainer
Inference Endpoints
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Dagobert42 commited on
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Push ../models/xlnet/xlnet-base-cased/biored-augmentations-only/ trained on biored-train_160_splits.pt (160 samples)

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Files changed (3) hide show
  1. README.md +23 -11
  2. model.safetensors +1 -1
  3. training_args.bin +2 -2
README.md CHANGED
@@ -28,12 +28,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the bigbio/biored dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1510
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- - Accuracy: 0.9508
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- - Precision: 0.8521
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- - Recall: 0.8278
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- - F1: 0.8391
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- - Weighted F1: 0.9506
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  ## Model description
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@@ -52,22 +52,34 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 2e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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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: 50
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Weighted F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------:|
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- | No log | 1.0 | 20 | 0.2068 | 0.9335 | 0.8641 | 0.7475 | 0.7976 | 0.9312 |
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- | No log | 2.0 | 40 | 0.1962 | 0.939 | 0.8035 | 0.8046 | 0.8013 | 0.9382 |
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- | No log | 3.0 | 60 | 0.1965 | 0.9429 | 0.8654 | 0.7947 | 0.826 | 0.9415 |
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- | No log | 4.0 | 80 | 0.1964 | 0.9443 | 0.8279 | 0.8174 | 0.8218 | 0.9436 |
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the bigbio/biored dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1552
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+ - Accuracy: 0.9545
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+ - Precision: 0.8651
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+ - Recall: 0.8306
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+ - F1: 0.8454
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+ - Weighted F1: 0.9544
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 1.8e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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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_ratio: 0.004
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  - num_epochs: 50
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Weighted F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------:|
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+ | No log | 0.5 | 10 | 0.2276 | 0.9252 | 0.7871 | 0.7482 | 0.7616 | 0.9233 |
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+ | No log | 1.0 | 20 | 0.2124 | 0.9318 | 0.8363 | 0.7571 | 0.7923 | 0.9298 |
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+ | No log | 1.5 | 30 | 0.2052 | 0.9342 | 0.8199 | 0.794 | 0.8057 | 0.9334 |
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+ | No log | 2.0 | 40 | 0.1958 | 0.9396 | 0.8132 | 0.8049 | 0.8038 | 0.9384 |
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+ | No log | 2.5 | 50 | 0.2043 | 0.9385 | 0.8162 | 0.8086 | 0.811 | 0.9377 |
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+ | No log | 3.0 | 60 | 0.1948 | 0.9409 | 0.8413 | 0.8109 | 0.8249 | 0.9404 |
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+ | No log | 3.5 | 70 | 0.1951 | 0.9436 | 0.8449 | 0.7963 | 0.8186 | 0.9425 |
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+ | No log | 4.0 | 80 | 0.2032 | 0.941 | 0.8169 | 0.8158 | 0.8158 | 0.9411 |
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+ | No log | 4.5 | 90 | 0.1984 | 0.944 | 0.827 | 0.8125 | 0.8194 | 0.9435 |
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+ | No log | 5.0 | 100 | 0.1982 | 0.9451 | 0.8313 | 0.8072 | 0.8184 | 0.9443 |
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+ | No log | 5.5 | 110 | 0.1968 | 0.9456 | 0.8249 | 0.8124 | 0.8178 | 0.945 |
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+ | No log | 6.0 | 120 | 0.2083 | 0.9432 | 0.8113 | 0.8173 | 0.8136 | 0.9429 |
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+ | No log | 6.5 | 130 | 0.2105 | 0.9441 | 0.8355 | 0.8132 | 0.8236 | 0.9436 |
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+ | No log | 7.0 | 140 | 0.2083 | 0.9439 | 0.8312 | 0.8207 | 0.8253 | 0.9439 |
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+ | No log | 7.5 | 150 | 0.2145 | 0.9447 | 0.8293 | 0.8051 | 0.8161 | 0.9437 |
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  ### Framework versions
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