robert_bilstm_mega_res-ner-resume-ner

This model is a fine-tuned version of hfl/chinese-roberta-wwm-ext-large on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.2496
• Precision: 0.9534
• Recall: 0.9609
• F1: 0.9572
• Accuracy: 0.9756

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: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0932	1.0	134	0.0917	0.9224	0.9496	0.9358	0.9734
0.0356	2.0	268	0.0776	0.9343	0.9635	0.9487	0.9777
0.0363	3.0	402	0.1026	0.9309	0.9479	0.9393	0.9727
0.0278	4.0	536	0.1041	0.9408	0.9531	0.9469	0.9750
0.0131	5.0	670	0.1194	0.9411	0.9574	0.9492	0.9746
0.0071	6.0	804	0.1491	0.9593	0.9618	0.9605	0.9768
0.0042	7.0	938	0.1474	0.9567	0.9609	0.9588	0.9749
0.0105	8.0	1072	0.1318	0.9527	0.9635	0.9581	0.9768
0.0039	9.0	1206	0.1616	0.9374	0.9496	0.9435	0.9724
0.0023	10.0	1340	0.1547	0.9452	0.9592	0.9521	0.9755
0.0026	11.0	1474	0.1585	0.9447	0.9505	0.9476	0.9746
0.0046	12.0	1608	0.1492	0.9411	0.9574	0.9492	0.9748
0.0051	13.0	1742	0.1440	0.9519	0.9626	0.9572	0.9755
0.0037	14.0	1876	0.1651	0.9516	0.9574	0.9545	0.9743
0.0006	15.0	2010	0.1641	0.9517	0.9583	0.9550	0.9755
0.0003	16.0	2144	0.1957	0.9536	0.9635	0.9585	0.9734
0.0002	17.0	2278	0.1896	0.9469	0.9600	0.9534	0.9734
0.0016	18.0	2412	0.1877	0.9419	0.9574	0.9496	0.9731
0.0001	19.0	2546	0.2117	0.9481	0.9531	0.9506	0.9722
0.0018	20.0	2680	0.2165	0.9540	0.9548	0.9544	0.9743
0.0011	21.0	2814	0.2082	0.9534	0.9592	0.9563	0.9719
0.0006	22.0	2948	0.2052	0.9567	0.9592	0.9579	0.9730
0.0001	23.0	3082	0.2016	0.9551	0.9600	0.9575	0.9746
0.0019	24.0	3216	0.1969	0.9549	0.9557	0.9553	0.9746
0.001	25.0	3350	0.1927	0.9516	0.9557	0.9536	0.9742
0.0002	26.0	3484	0.1767	0.9601	0.9609	0.9605	0.9752
0.0003	27.0	3618	0.1936	0.9433	0.9540	0.9486	0.9735
0.0011	28.0	3752	0.1928	0.9425	0.9548	0.9486	0.9734
0.0001	29.0	3886	0.2076	0.9456	0.9522	0.9489	0.9739
0.0052	30.0	4020	0.2040	0.9418	0.9557	0.9487	0.9748
0.0013	31.0	4154	0.1716	0.9552	0.9635	0.9593	0.9749
0.0004	32.0	4288	0.1762	0.9475	0.9566	0.9520	0.9755
0.0004	33.0	4422	0.1479	0.9450	0.9557	0.9503	0.9768
0.0001	34.0	4556	0.1668	0.9469	0.9609	0.9539	0.9766
0.0019	35.0	4690	0.1884	0.9508	0.9574	0.9541	0.9766
0.0001	36.0	4824	0.1873	0.9451	0.9566	0.9508	0.9748
0.0012	37.0	4958	0.1807	0.9553	0.9652	0.9602	0.9763
0.0071	38.0	5092	0.1993	0.9558	0.9592	0.9575	0.9760
0.0	39.0	5226	0.1821	0.9483	0.9557	0.9520	0.9751
0.0008	40.0	5360	0.1968	0.9576	0.9626	0.9601	0.9757
0.0011	41.0	5494	0.1930	0.9493	0.9600	0.9546	0.9751
0.0016	42.0	5628	0.1974	0.9476	0.9592	0.9534	0.9753
0.0	43.0	5762	0.1935	0.9477	0.9600	0.9538	0.9759
0.0015	44.0	5896	0.1970	0.9525	0.9583	0.9554	0.9757
0.0008	45.0	6030	0.2079	0.9542	0.9592	0.9567	0.9775
0.0003	46.0	6164	0.1899	0.9435	0.9583	0.9509	0.9746
0.0047	47.0	6298	0.1822	0.9478	0.9626	0.9552	0.9748
0.0019	48.0	6432	0.1990	0.9543	0.9609	0.9576	0.9758
0.001	49.0	6566	0.2001	0.9518	0.9609	0.9563	0.9764
0.0	50.0	6700	0.1961	0.9493	0.9600	0.9546	0.9758
0.0	51.0	6834	0.2083	0.9551	0.9618	0.9584	0.9765
0.0	52.0	6968	0.2072	0.9558	0.9583	0.9570	0.9762
0.0	53.0	7102	0.2112	0.9516	0.9574	0.9545	0.9764
0.0001	54.0	7236	0.2160	0.9560	0.9618	0.9589	0.9774
0.0	55.0	7370	0.2166	0.9560	0.9618	0.9589	0.9771
0.0	56.0	7504	0.2188	0.9560	0.9618	0.9589	0.9770
0.0	57.0	7638	0.2208	0.9568	0.9618	0.9593	0.9771
0.0007	58.0	7772	0.2301	0.9559	0.9600	0.9580	0.9764
0.0	59.0	7906	0.2317	0.9551	0.9600	0.9575	0.9764
0.0	60.0	8040	0.2326	0.9559	0.9600	0.9580	0.9759
0.0	61.0	8174	0.2349	0.9583	0.9592	0.9587	0.9761
0.0	62.0	8308	0.2378	0.9550	0.9592	0.9571	0.9768
0.0	63.0	8442	0.2406	0.9526	0.9600	0.9563	0.9767
0.0	64.0	8576	0.2367	0.9584	0.9600	0.9592	0.9771
0.0009	65.0	8710	0.2329	0.9584	0.9600	0.9592	0.9772
0.0	66.0	8844	0.2376	0.9524	0.9566	0.9545	0.9761
0.0009	67.0	8978	0.2381	0.9509	0.9583	0.9546	0.9759
0.0	68.0	9112	0.2236	0.9585	0.9644	0.9615	0.9778
0.0	69.0	9246	0.2194	0.9619	0.9644	0.9631	0.9769
0.0	70.0	9380	0.2192	0.9595	0.9670	0.9632	0.9775
0.0	71.0	9514	0.2124	0.9562	0.9679	0.9620	0.9777
0.0007	72.0	9648	0.2139	0.9587	0.9679	0.9633	0.9777
0.0	73.0	9782	0.2266	0.9552	0.9644	0.9598	0.9764
0.0	74.0	9916	0.2275	0.9593	0.9635	0.9614	0.9765
0.0	75.0	10050	0.2239	0.9577	0.9644	0.9610	0.9774
0.0	76.0	10184	0.2212	0.9578	0.9652	0.9615	0.9760
0.0	77.0	10318	0.2208	0.9560	0.9635	0.9598	0.9758
0.0	78.0	10452	0.2259	0.9577	0.9635	0.9606	0.9757
0.0	79.0	10586	0.2279	0.9593	0.9635	0.9614	0.9756
0.0	80.0	10720	0.2294	0.9577	0.9635	0.9606	0.9756
0.0	81.0	10854	0.2380	0.9492	0.9574	0.9533	0.9766
0.0	82.0	10988	0.2405	0.9577	0.9644	0.9610	0.9759
0.0	83.0	11122	0.2442	0.9511	0.9635	0.9573	0.9751
0.0003	84.0	11256	0.2371	0.9552	0.9626	0.9589	0.9749
0.0	85.0	11390	0.2499	0.9559	0.9609	0.9584	0.9750
0.0	86.0	11524	0.2516	0.9601	0.9626	0.9614	0.9752
0.0	87.0	11658	0.2519	0.9593	0.9626	0.9610	0.9752
0.0008	88.0	11792	0.2516	0.9576	0.9618	0.9597	0.9752
0.0007	89.0	11926	0.2517	0.9593	0.9626	0.9610	0.9753
0.0	90.0	12060	0.2477	0.9559	0.9609	0.9584	0.9754
0.0	91.0	12194	0.2477	0.9559	0.9609	0.9584	0.9754
0.0	92.0	12328	0.2479	0.9543	0.9609	0.9576	0.9755
0.0007	93.0	12462	0.2482	0.9543	0.9609	0.9576	0.9754
0.0	94.0	12596	0.2487	0.9534	0.9609	0.9572	0.9755
0.0	95.0	12730	0.2490	0.9534	0.9609	0.9572	0.9755
0.0	96.0	12864	0.2495	0.9534	0.9609	0.9572	0.9755
0.0	97.0	12998	0.2489	0.9534	0.9609	0.9572	0.9755
0.0	98.0	13132	0.2495	0.9534	0.9609	0.9572	0.9756
0.0006	99.0	13266	0.2496	0.9534	0.9609	0.9572	0.9756
0.0	100.0	13400	0.2496	0.9534	0.9609	0.9572	0.9756

Framework versions

• Transformers 4.46.2
• Pytorch 2.4.1+cu124
• Datasets 3.1.0
• Tokenizers 0.20.3