mlcovid19-classifier

oscarwu/mlcovid19-classifier

mlcovid19-classifier is a text classification model from oscarwu. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Text Classification
Library: transformers
License: mit
Model type: xlm-roberta
Access: Public
Created: Oct 10, 2022
Updated: Oct 18, 2022
SHA: 0c5a746c

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Model card

From the Hugging Face model README

mlcovid19-classifier

This model is a fine-tuned version of oscarwu/mlcovid19-classifier on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.2879
F1 Macro: 0.7978
F1 Misinformation: 0.9347
F1 Factual: 0.9423
F1 Other: 0.5166
Prec Macro: 0.8156
Prec Misinformation: 0.9277
Prec Factual: 0.9345
Prec Other: 0.5846

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: 128
eval_batch_size: 128
seed: 42
gradient_accumulation_steps: 32
total_train_batch_size: 4096
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 2607
num_epochs: 400

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Macro	F1 Misinformation	F1 Factual	F1 Other	Prec Macro	Prec Misinformation	Prec Factual	Prec Other
0.4535	1.98	10	0.4122	0.6809	0.8906	0.8993	0.2529	0.7749	0.8433	0.9169	0.5646
0.4445	3.98	20	0.4056	0.6844	0.8918	0.9004	0.2611	0.7706	0.8461	0.9171	0.5487
0.4362	5.98	30	0.3966	0.6870	0.8930	0.9020	0.2658	0.7672	0.8490	0.9171	0.5356
0.4229	7.98	40	0.3864	0.6885	0.8955	0.9055	0.2645	0.7652	0.8531	0.9179	0.5246
0.4134	9.98	50	0.3774	0.6889	0.8983	0.9091	0.2594	0.7697	0.8573	0.9173	0.5345
0.4004	11.98	60	0.3682	0.6907	0.8996	0.9111	0.2616	0.7763	0.8605	0.9148	0.5536
0.3893	13.98	70	0.3583	0.6960	0.9014	0.9124	0.2740	0.7853	0.8629	0.9152	0.5778
0.3853	15.98	80	0.3483	0.7036	0.9031	0.9157	0.2920	0.7749	0.8683	0.9172	0.5390
0.369	17.98	90	0.3399	0.7011	0.9037	0.9167	0.2828	0.7775	0.8690	0.9159	0.5476
0.36	19.98	100	0.3312	0.7102	0.9056	0.9194	0.3055	0.7836	0.8733	0.9167	0.5609
0.3445	21.98	110	0.3237	0.7116	0.9065	0.9204	0.3078	0.7860	0.8749	0.9165	0.5667
0.3406	23.98	120	0.3181	0.7058	0.9068	0.9212	0.2893	0.7880	0.8740	0.9162	0.5738
0.3286	25.98	130	0.3094	0.7183	0.9099	0.9250	0.32	0.7932	0.8782	0.9216	0.5797
0.3213	27.98	140	0.3049	0.7187	0.9111	0.9254	0.3196	0.7957	0.8800	0.9204	0.5867
0.3111	29.98	150	0.3017	0.7219	0.9129	0.9264	0.3263	0.7983	0.8843	0.9178	0.5927
0.3087	31.98	160	0.2970	0.7231	0.9132	0.9276	0.3287	0.7977	0.8850	0.9188	0.5893
0.2992	33.98	170	0.2926	0.7243	0.9141	0.9293	0.3293	0.8003	0.8839	0.9235	0.5935
0.2924	35.98	180	0.2892	0.7312	0.9150	0.9303	0.3482	0.7971	0.8889	0.9218	0.5806
0.2878	37.98	190	0.2870	0.7356	0.9173	0.9324	0.3571	0.8027	0.8906	0.9246	0.5929
0.2811	39.98	200	0.2844	0.7439	0.9188	0.9328	0.3801	0.8109	0.8954	0.9213	0.6161
0.2751	41.98	210	0.2816	0.7500	0.9197	0.9340	0.3963	0.8060	0.8973	0.9250	0.5956
0.2683	43.98	220	0.2798	0.7517	0.9210	0.9339	0.4000	0.8068	0.8976	0.9272	0.5956
0.2643	45.98	230	0.2766	0.7544	0.9221	0.9349	0.4062	0.8064	0.8990	0.9290	0.5910
0.2619	47.98	240	0.2736	0.7579	0.9227	0.9356	0.4155	0.8085	0.9002	0.9298	0.5954
0.2539	49.98	250	0.2733	0.7567	0.9231	0.9357	0.4111	0.8060	0.9006	0.9302	0.5872
0.2496	51.98	260	0.2713	0.7600	0.9235	0.9360	0.4206	0.8070	0.9009	0.9320	0.5881
0.2455	53.98	270	0.2697	0.7575	0.9231	0.9356	0.4139	0.8052	0.9009	0.9304	0.5844
0.2371	55.98	280	0.2686	0.7652	0.9239	0.9356	0.4360	0.8058	0.9058	0.9283	0.5833
0.2316	57.98	290	0.2686	0.7664	0.9243	0.9361	0.4389	0.8037	0.9073	0.9288	0.5749
0.2258	59.98	300	0.2664	0.7680	0.9247	0.9360	0.4431	0.8018	0.9095	0.9279	0.5680
0.2207	61.98	310	0.2663	0.7736	0.9262	0.9373	0.4574	0.8015	0.9145	0.9276	0.5625
0.2167	63.98	320	0.2643	0.7715	0.9268	0.9380	0.4498	0.8003	0.9127	0.9312	0.5571
0.2131	65.98	330	0.2627	0.7753	0.9287	0.9398	0.4573	0.8064	0.9123	0.9356	0.5714
0.2075	67.98	340	0.2644	0.7760	0.9290	0.9397	0.4593	0.8056	0.9136	0.9349	0.5682
0.2049	69.98	350	0.2648	0.7768	0.9290	0.9390	0.4623	0.8050	0.9174	0.9292	0.5685
0.2016	71.98	360	0.2631	0.7771	0.9295	0.9394	0.4623	0.8055	0.9165	0.9316	0.5685
0.1979	73.98	370	0.2644	0.7793	0.9305	0.9397	0.4677	0.8041	0.9208	0.9295	0.5620
0.1939	75.98	380	0.2671	0.7909	0.9312	0.9392	0.5023	0.8099	0.9272	0.9256	0.5771
0.1932	77.98	390	0.2648	0.7927	0.9325	0.9422	0.5035	0.8104	0.9242	0.9361	0.5709
0.1856	79.98	400	0.2615	0.7922	0.9331	0.9431	0.5004	0.8111	0.9235	0.9379	0.5719
0.1837	81.98	410	0.2624	0.7898	0.9328	0.9447	0.4920	0.8141	0.9183	0.9432	0.5808
0.1781	83.98	420	0.2660	0.7988	0.9334	0.9432	0.5196	0.8128	0.9263	0.9388	0.5733
0.172	85.98	430	0.2642	0.7909	0.9335	0.9428	0.4964	0.8139	0.9234	0.9353	0.5829
0.172	87.98	440	0.2695	0.7880	0.9321	0.9430	0.4889	0.8121	0.9172	0.9422	0.5771
0.1656	89.98	450	0.2671	0.7928	0.9337	0.9436	0.5012	0.8145	0.9212	0.9411	0.5811
0.163	91.98	460	0.2693	0.7949	0.9331	0.9429	0.5088	0.8111	0.9232	0.9408	0.5692
0.1555	93.98	470	0.2696	0.7967	0.9332	0.9431	0.5138	0.8142	0.9203	0.9449	0.5776
0.1513	95.98	480	0.2710	0.7985	0.9340	0.9443	0.5172	0.8156	0.9220	0.9450	0.5798
0.1478	97.98	490	0.2722	0.7991	0.9342	0.9442	0.5189	0.8138	0.9243	0.9436	0.5736
0.1435	99.98	500	0.2725	0.7981	0.9343	0.9432	0.5166	0.8124	0.9248	0.9424	0.57
0.1409	101.98	510	0.2754	0.7994	0.9345	0.9432	0.5206	0.8161	0.9231	0.9433	0.5819
0.1384	103.98	520	0.2817	0.7991	0.9347	0.9441	0.5184	0.8166	0.9233	0.9436	0.5828
0.1333	105.98	530	0.2833	0.7934	0.9351	0.9434	0.5016	0.8178	0.9232	0.9380	0.5921
0.1267	107.98	540	0.2929	0.7884	0.9337	0.9429	0.4886	0.8167	0.9198	0.9377	0.5925
0.1234	109.98	550	0.2879	0.7978	0.9347	0.9423	0.5166	0.8156	0.9277	0.9345	0.5846

Framework versions

Transformers 4.23.1
Pytorch 1.12.1+cu113
Datasets 2.6.1
Tokenizers 0.13.1

Task: Text Classification
Library: transformers
Type: xlm-roberta
License: mit
Created: Oct 10, 2022
Updated: Oct 18, 2022