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gonzalezrostani/
my_awesome_wnut_US

Name: my_awesome_wnut_US
Availability: InStock

generated_from_trainer

distilbert/distilbert-base-uncased

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my_awesome_wnut_US

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

Loss: 0.2416
Precision: 0.8182
Recall: 0.72
F1: 0.7660
Accuracy: 0.9754

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	46	0.1244	0.9	0.576	0.7024	0.9617
No log	2.0	92	0.0860	0.7459	0.728	0.7368	0.9690
No log	3.0	138	0.0826	0.8426	0.728	0.7811	0.9741
No log	4.0	184	0.0872	0.7724	0.76	0.7661	0.9751
No log	5.0	230	0.1060	0.8333	0.72	0.7725	0.9757
No log	6.0	276	0.1325	0.8381	0.704	0.7652	0.9748
No log	7.0	322	0.1462	0.7807	0.712	0.7448	0.9735
No log	8.0	368	0.1417	0.775	0.744	0.7592	0.9741
No log	9.0	414	0.1480	0.8017	0.744	0.7718	0.9754
No log	10.0	460	0.1487	0.7949	0.744	0.7686	0.9757
0.0367	11.0	506	0.1699	0.7845	0.728	0.7552	0.9751
0.0367	12.0	552	0.1739	0.7686	0.744	0.7561	0.9738
0.0367	13.0	598	0.1741	0.7863	0.736	0.7603	0.9754
0.0367	14.0	644	0.1859	0.7826	0.72	0.7500	0.9754
0.0367	15.0	690	0.1914	0.7965	0.72	0.7563	0.9745
0.0367	16.0	736	0.1843	0.7344	0.752	0.7431	0.9729
0.0367	17.0	782	0.1922	0.76	0.76	0.76	0.9738
0.0367	18.0	828	0.1946	0.7308	0.76	0.7451	0.9729
0.0367	19.0	874	0.2102	0.7759	0.72	0.7469	0.9738
0.0367	20.0	920	0.2074	0.7339	0.728	0.7309	0.9726
0.0367	21.0	966	0.2072	0.7273	0.768	0.7471	0.9726
0.0007	22.0	1012	0.2005	0.7121	0.752	0.7315	0.9732
0.0007	23.0	1058	0.1960	0.75	0.744	0.7470	0.9745
0.0007	24.0	1104	0.2443	0.8627	0.704	0.7753	0.9726
0.0007	25.0	1150	0.1927	0.8349	0.728	0.7778	0.9754
0.0007	26.0	1196	0.1969	0.7863	0.736	0.7603	0.9735
0.0007	27.0	1242	0.1964	0.7769	0.752	0.7642	0.9748
0.0007	28.0	1288	0.2002	0.7913	0.728	0.7583	0.9745
0.0007	29.0	1334	0.1866	0.7521	0.728	0.7398	0.9726
0.0007	30.0	1380	0.1871	0.7863	0.736	0.7603	0.9748
0.0007	31.0	1426	0.1751	0.7769	0.752	0.7642	0.9738
0.0007	32.0	1472	0.1853	0.8087	0.744	0.775	0.9751
0.0012	33.0	1518	0.2058	0.8125	0.728	0.7679	0.9751
0.0012	34.0	1564	0.2278	0.8447	0.696	0.7632	0.9738
0.0012	35.0	1610	0.2008	0.8070	0.736	0.7699	0.9760
0.0012	36.0	1656	0.2150	0.8148	0.704	0.7554	0.9748
0.0012	37.0	1702	0.2135	0.8378	0.744	0.7881	0.9760
0.0012	38.0	1748	0.2158	0.8378	0.744	0.7881	0.9760
0.0012	39.0	1794	0.2116	0.8319	0.752	0.7899	0.9760
0.0012	40.0	1840	0.2146	0.8319	0.752	0.7899	0.9760
0.0012	41.0	1886	0.2180	0.8230	0.744	0.7815	0.9757
0.0012	42.0	1932	0.2228	0.8304	0.744	0.7848	0.9754
0.0012	43.0	1978	0.2223	0.8087	0.744	0.775	0.9751
0.0004	44.0	2024	0.2251	0.8158	0.744	0.7782	0.9754
0.0004	45.0	2070	0.2298	0.8241	0.712	0.7639	0.9751
0.0004	46.0	2116	0.2297	0.8224	0.704	0.7586	0.9745
0.0004	47.0	2162	0.2205	0.8182	0.72	0.7660	0.9751
0.0004	48.0	2208	0.2217	0.8182	0.72	0.7660	0.9751
0.0004	49.0	2254	0.2301	0.8411	0.72	0.7759	0.9757
0.0004	50.0	2300	0.2321	0.8411	0.72	0.7759	0.9754
0.0004	51.0	2346	0.2291	0.8333	0.72	0.7725	0.9760
0.0004	52.0	2392	0.2298	0.8333	0.72	0.7725	0.9760
0.0004	53.0	2438	0.2231	0.8198	0.728	0.7712	0.9760
0.0004	54.0	2484	0.2289	0.8224	0.704	0.7586	0.9754
0.0003	55.0	2530	0.2374	0.8302	0.704	0.7619	0.9754
0.0003	56.0	2576	0.2201	0.8165	0.712	0.7607	0.9751
0.0003	57.0	2622	0.2259	0.8241	0.712	0.7639	0.9757
0.0003	58.0	2668	0.2119	0.7705	0.752	0.7611	0.9751
0.0003	59.0	2714	0.2147	0.7769	0.752	0.7642	0.9754
0.0003	60.0	2760	0.2195	0.7949	0.744	0.7686	0.9757
0.0003	61.0	2806	0.2269	0.8125	0.728	0.7679	0.9763
0.0003	62.0	2852	0.2329	0.8182	0.72	0.7660	0.9757
0.0003	63.0	2898	0.2348	0.8182	0.72	0.7660	0.9757
0.0003	64.0	2944	0.2359	0.8182	0.72	0.7660	0.9757
0.0003	65.0	2990	0.2303	0.8182	0.72	0.7660	0.9763
0.0002	66.0	3036	0.2408	0.8396	0.712	0.7706	0.9748
0.0002	67.0	3082	0.2399	0.8333	0.72	0.7725	0.9754
0.0002	68.0	3128	0.2390	0.8182	0.72	0.7660	0.9760
0.0002	69.0	3174	0.2399	0.8257	0.72	0.7692	0.9757
0.0002	70.0	3220	0.2386	0.8257	0.72	0.7692	0.9757
0.0002	71.0	3266	0.2383	0.8182	0.72	0.7660	0.9760
0.0002	72.0	3312	0.2356	0.8182	0.72	0.7660	0.9763
0.0002	73.0	3358	0.2652	0.8614	0.696	0.7699	0.9729
0.0002	74.0	3404	0.2383	0.8257	0.72	0.7692	0.9748
0.0002	75.0	3450	0.2400	0.8333	0.72	0.7725	0.9751
0.0002	76.0	3496	0.2429	0.8333	0.72	0.7725	0.9751
0.0002	77.0	3542	0.2459	0.8333	0.72	0.7725	0.9751
0.0002	78.0	3588	0.2470	0.8411	0.72	0.7759	0.9748
0.0002	79.0	3634	0.2599	0.8462	0.704	0.7686	0.9745
0.0002	80.0	3680	0.2529	0.8491	0.72	0.7792	0.9748
0.0002	81.0	3726	0.2448	0.8318	0.712	0.7672	0.9751
0.0002	82.0	3772	0.2447	0.8333	0.72	0.7725	0.9754
0.0002	83.0	3818	0.2455	0.8318	0.712	0.7672	0.9751
0.0002	84.0	3864	0.2456	0.8333	0.72	0.7725	0.9754
0.0002	85.0	3910	0.2456	0.8257	0.72	0.7692	0.9751
0.0002	86.0	3956	0.2606	0.8431	0.688	0.7577	0.9745
0.0001	87.0	4002	0.2588	0.8462	0.704	0.7686	0.9751
0.0001	88.0	4048	0.2569	0.8738	0.72	0.7895	0.9757
0.0001	89.0	4094	0.2552	0.8571	0.72	0.7826	0.9751
0.0001	90.0	4140	0.2539	0.8491	0.72	0.7792	0.9748
0.0001	91.0	4186	0.2537	0.8491	0.72	0.7792	0.9748
0.0001	92.0	4232	0.2448	0.8257	0.72	0.7692	0.9757
0.0001	93.0	4278	0.2450	0.8257	0.72	0.7692	0.9757
0.0001	94.0	4324	0.2454	0.8257	0.72	0.7692	0.9757
0.0001	95.0	4370	0.2455	0.8257	0.72	0.7692	0.9757
0.0001	96.0	4416	0.2454	0.8257	0.72	0.7692	0.9757
0.0001	97.0	4462	0.2415	0.8182	0.72	0.7660	0.9754
0.0002	98.0	4508	0.2414	0.8182	0.72	0.7660	0.9754
0.0002	99.0	4554	0.2415	0.8182	0.72	0.7660	0.9754
0.0002	100.0	4600	0.2416	0.8182	0.72	0.7660	0.9754

Framework versions

Transformers 4.39.1
Pytorch 2.2.2+cu118
Datasets 2.18.0
Tokenizers 0.15.2

gonzalezrostani/my_awesome_wnut_US

my_awesome_wnut_US

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gonzalezrostani/
my_awesome_wnut_US