README.md

metadata

library_name: transformers
license: mit
base_model: camembert-base
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
model-index:
  - name: v2-camembert-ner-job-ads
    results: []

v2-camembert-ner-job-ads

This model is a fine-tuned version of camembert-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.5342
• Precision: 0.3948
• Recall: 0.7311
• F1: 0.5127
• F2: 0.6247

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: 2e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 741
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	F2
3.0518	1.0	121	2.8715	0.0	0.0	0.0	0
2.5717	2.0	242	2.2493	0.3210	0.1778	0.2288	0.1952
2.0235	3.0	363	1.8607	0.2572	0.4860	0.3364	0.4126
1.7860	4.0	484	1.6046	0.2161	0.5011	0.3019	0.3965
1.4654	5.0	605	1.3541	0.2824	0.6345	0.3908	0.5078
1.2812	6.0	726	1.1570	0.2624	0.6150	0.3678	0.4847
1.0853	7.0	847	0.9783	0.2889	0.6503	0.4001	0.5202
0.8719	8.0	968	0.8576	0.2611	0.6552	0.3734	0.5032
0.7604	9.0	1089	0.7621	0.3454	0.6951	0.4615	0.5781
0.6377	10.0	1210	0.6978	0.3358	0.6871	0.4511	0.5682
0.5671	11.0	1331	0.6360	0.3287	0.6903	0.4454	0.5658
0.4867	12.0	1452	0.6012	0.3483	0.7055	0.4663	0.5854
0.4340	13.0	1573	0.5768	0.3330	0.7059	0.4525	0.5767
0.4057	14.0	1694	0.5495	0.3528	0.6907	0.4670	0.5797
0.3688	15.0	1815	0.5370	0.3739	0.7218	0.4926	0.6085
0.3279	16.0	1936	0.5372	0.3589	0.7142	0.4777	0.5961
0.3076	17.0	2057	0.5402	0.3702	0.7285	0.4910	0.6104
0.2874	18.0	2178	0.5484	0.3994	0.7205	0.5139	0.6207
0.2774	19.0	2299	0.5410	0.4021	0.7315	0.5190	0.6285
0.2600	20.0	2420	0.5429	0.4011	0.7340	0.5188	0.6295
0.2505	21.0	2541	0.5525	0.4175	0.7321	0.5317	0.6362
0.2399	22.0	2662	0.5436	0.4165	0.7323	0.5310	0.6359
0.2298	23.0	2783	0.5315	0.4045	0.7326	0.5212	0.6303
0.2266	24.0	2904	0.5342	0.3948	0.7311	0.5127	0.6247

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 4.8.3
• Tokenizers 0.22.2