Hugging Face's logo

ali2066
/
DistilBERTFINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False

Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
ali2066 commited on
Commit
58612b5
·
1 Parent(s): 4089be7

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +9 -10
README.md CHANGED
@@ -1,5 +1,4 @@
1
  ---
2
- license: apache-2.0
3
  tags:
4
  - generated_from_trainer
5
  metrics:
@@ -17,11 +16,11 @@ should probably proofread and complete it, then remove this comment. -->
17
 
18
  # DistilBERTFINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False
19
 
20
- This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
21
  It achieves the following results on the evaluation set:
22
- - Loss: 0.0658
23
- - Precision: 0.9900
24
- - Recall: 0.9908
25
  - F1: 0.9904
26
  - Accuracy: 0.9814
27
 
@@ -54,11 +53,11 @@ The following hyperparameters were used during training:
54
 
55
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
56
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
57
- | No log | 1.0 | 479 | 0.3565 | 0.8698 | 0.9243 | 0.8962 | 0.8371 |
58
- | 0.4677 | 2.0 | 958 | 0.3354 | 0.8894 | 0.9347 | 0.9115 | 0.8620 |
59
- | 0.296 | 3.0 | 1437 | 0.3268 | 0.8931 | 0.9269 | 0.9097 | 0.8600 |
60
- | 0.2264 | 4.0 | 1916 | 0.3519 | 0.8961 | 0.9347 | 0.9150 | 0.8679 |
61
- | 0.1729 | 5.0 | 2395 | 0.3626 | 0.8960 | 0.9334 | 0.9143 | 0.8669 |
62
 
63
 
64
  ### Framework versions
 
1
  ---
 
2
  tags:
3
  - generated_from_trainer
4
  metrics:
 
16
 
17
  # DistilBERTFINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False
18
 
19
+ This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset.
20
  It achieves the following results on the evaluation set:
21
+ - Loss: 0.0557
22
+ - Precision: 0.9930
23
+ - Recall: 0.9878
24
  - F1: 0.9904
25
  - Accuracy: 0.9814
26
 
 
53
 
54
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
55
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
56
+ | No log | 1.0 | 479 | 0.3334 | 0.9041 | 0.9041 | 0.9041 | 0.8550 |
57
+ | 0.3756 | 2.0 | 958 | 0.3095 | 0.8991 | 0.9251 | 0.9119 | 0.8649 |
58
+ | 0.2653 | 3.0 | 1437 | 0.3603 | 0.8929 | 0.9527 | 0.9218 | 0.8779 |
59
+ | 0.1991 | 4.0 | 1916 | 0.3907 | 0.8919 | 0.9540 | 0.9219 | 0.8779 |
60
+ | 0.1586 | 5.0 | 2395 | 0.3642 | 0.9070 | 0.9356 | 0.9211 | 0.8788 |
61
 
62
 
63
  ### Framework versions