metadata

base_model: vinai/phobert-base-v2
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - recall
  - precision
model-index:
  - name: cls-comment-phobert-base-v2-v3.2.1
    results: []

cls-comment-phobert-base-v2-v3.2.1

This model is a fine-tuned version of vinai/phobert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.2873
Accuracy: 0.9323
F1 Score: 0.9262
Recall: 0.9217
Precision: 0.9320

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: 1e-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
training_steps: 4000

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Score	Recall	Precision
1.8947	0.8696	100	1.6875	0.4001	0.0832	0.1437	0.1464
1.5395	1.7391	200	1.2897	0.5849	0.2356	0.2632	0.2752
1.1205	2.6087	300	0.8468	0.7999	0.5833	0.5810	0.5890
0.82	3.4783	400	0.6537	0.8369	0.6179	0.6355	0.6062
0.6232	4.3478	500	0.5371	0.8538	0.6337	0.6518	0.7525
0.5148	5.2174	600	0.4651	0.8728	0.7299	0.7211	0.7549
0.4204	6.0870	700	0.4010	0.8869	0.7654	0.7712	0.8914
0.3421	6.9565	800	0.3648	0.9051	0.8714	0.8588	0.8941
0.2841	7.8261	900	0.3240	0.9182	0.9007	0.9038	0.8978
0.2319	8.6957	1000	0.3025	0.9204	0.9061	0.8976	0.9175
0.205	9.5652	1100	0.2986	0.9209	0.9099	0.9086	0.9123
0.1783	10.4348	1200	0.3047	0.9206	0.9104	0.9207	0.9025
0.1587	11.3043	1300	0.2758	0.9296	0.9203	0.9177	0.9233
0.1286	12.1739	1400	0.2927	0.9266	0.9144	0.9199	0.9101
0.1221	13.0435	1500	0.2821	0.9318	0.9245	0.9194	0.9309
0.1087	13.9130	1600	0.2789	0.9293	0.9160	0.9237	0.9090
0.0982	14.7826	1700	0.2834	0.9291	0.9196	0.9213	0.9188
0.089	15.6522	1800	0.2828	0.9299	0.9202	0.9261	0.9152
0.0795	16.5217	1900	0.2737	0.9331	0.9244	0.9239	0.9253
0.0684	17.3913	2000	0.2873	0.9323	0.9262	0.9217	0.9320
0.0673	18.2609	2100	0.2904	0.9320	0.9252	0.9184	0.9333
0.0571	19.1304	2200	0.3166	0.9293	0.9222	0.9210	0.9251
0.0561	20.0	2300	0.2922	0.9318	0.9221	0.9298	0.9150
0.0511	20.8696	2400	0.2993	0.9315	0.9191	0.9303	0.9088
0.0442	21.7391	2500	0.3201	0.9266	0.9162	0.9280	0.9060
0.0447	22.6087	2600	0.3155	0.9282	0.9137	0.9282	0.9010
0.0415	23.4783	2700	0.3018	0.9334	0.9226	0.9270	0.9185
0.0359	24.3478	2800	0.3192	0.9299	0.9177	0.9308	0.9063
0.0369	25.2174	2900	0.3064	0.9337	0.9211	0.9286	0.9141
0.0296	26.0870	3000	0.3110	0.9329	0.9237	0.9279	0.9198

Framework versions

Transformers 4.40.1
Pytorch 2.2.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1