metadata

base_model: mistral_rulm_unigram_init_20_10_23
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: mistral7b_darulm_unigram_1e_20_10_23
    results: []

mistral7b_darulm_unigram_1e_20_10_23

This model is a fine-tuned version of mistral_rulm_unigram_init_20_10_23 on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.7017
Accuracy: 0.4706

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: 0.0003
train_batch_size: 6
eval_batch_size: 6
seed: 42
distributed_type: multi-GPU
num_devices: 10
gradient_accumulation_steps: 4
total_train_batch_size: 240
total_eval_batch_size: 60
optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 200
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
3.1631	0.01	1000	3.1804	0.4182
3.1124	0.02	2000	3.1065	0.4272
3.0757	0.03	3000	3.0894	0.4288
3.0488	0.04	4000	3.0630	0.4319
3.0403	0.05	5000	3.0423	0.4336
3.0172	0.05	6000	3.0384	0.4343
3.0102	0.06	7000	3.0267	0.4360
2.9888	0.07	8000	3.0190	0.4361
3.0024	0.08	9000	3.0040	0.4385
2.9948	0.09	10000	3.0058	0.4378
2.9774	0.1	11000	2.9962	0.4389
2.9818	0.11	12000	2.9964	0.4390
2.9771	0.12	13000	2.9913	0.4396
2.9786	0.13	14000	2.9915	0.4391
2.9866	0.14	15000	2.9924	0.4394
2.9751	0.14	16000	2.9918	0.4389
2.9702	0.15	17000	2.9926	0.4393
2.9695	0.16	18000	2.9816	0.4401
2.9615	0.17	19000	2.9826	0.4402
2.9609	0.18	20000	2.9791	0.4406
2.9607	0.19	21000	2.9684	0.4416
2.9533	0.2	22000	2.9677	0.4422
2.9513	0.21	23000	2.9676	0.4421
2.9563	0.22	24000	2.9610	0.4429
2.9466	0.23	25000	2.9627	0.4424
2.9431	0.24	26000	2.9590	0.4424
2.9412	0.24	27000	2.9525	0.4436
2.9299	0.25	28000	2.9504	0.4435
2.9332	0.26	29000	2.9486	0.4435
2.9255	0.27	30000	2.9425	0.4442
2.9242	0.28	31000	2.9459	0.4434
2.9242	0.29	32000	2.9378	0.4445
2.9267	0.3	33000	2.9316	0.4453
2.9151	0.31	34000	2.9315	0.4454
2.9105	0.32	35000	2.9286	0.4456
2.9053	0.33	36000	2.9242	0.4457
2.9023	0.33	37000	2.9195	0.4466
2.8946	0.34	38000	2.9177	0.4468
2.9037	0.35	39000	2.9147	0.4470
2.8893	0.36	40000	2.9130	0.4468
2.8891	0.37	41000	2.9055	0.4481
2.8851	0.38	42000	2.9017	0.4485
2.8909	0.39	43000	2.9011	0.4483
2.896	0.4	44000	2.9061	0.4479
2.8918	0.41	45000	2.9043	0.4479
2.8847	0.42	46000	2.8954	0.4490
2.8749	0.42	47000	2.8912	0.4494
2.8832	0.43	48000	2.8912	0.4496
2.8745	0.44	49000	2.8853	0.4500
2.8717	0.45	50000	2.8834	0.4502
2.8659	0.46	51000	2.8831	0.4503
2.865	0.47	52000	2.8784	0.4505
2.8575	0.48	53000	2.8763	0.4508
2.8571	0.49	54000	2.8741	0.4513
2.8554	0.5	55000	2.8704	0.4514
2.8526	0.51	56000	2.8669	0.4519
2.8521	0.52	57000	2.8618	0.4525
2.8398	0.52	58000	2.8600	0.4522
2.8398	0.53	59000	2.8576	0.4528
2.837	0.54	60000	2.8536	0.4528
2.837	0.55	61000	2.8519	0.4535
2.8427	0.56	62000	2.8493	0.4536
2.8365	0.57	63000	2.8468	0.4541
2.8327	0.58	64000	2.8447	0.4539
2.8289	0.59	65000	2.8388	0.4546
2.8166	0.6	66000	2.8346	0.4547
2.8171	0.61	67000	2.8294	0.4558
2.8184	0.61	68000	2.8269	0.4556
2.8102	0.62	69000	2.8243	0.4563
2.8153	0.63	70000	2.8211	0.4564
2.8035	0.64	71000	2.8185	0.4569
2.8042	0.65	72000	2.8206	0.4569
2.7984	0.66	73000	2.8138	0.4574
2.7883	0.67	74000	2.8112	0.4574
2.7962	0.68	75000	2.8056	0.4584
2.7937	0.69	76000	2.8068	0.4582
2.7853	0.7	77000	2.8011	0.4588
2.7798	0.71	78000	2.7954	0.4597
2.7851	0.71	79000	2.7913	0.4598
2.7831	0.72	80000	2.7897	0.4600
2.7773	0.73	81000	2.7862	0.4603
2.7688	0.74	82000	2.7836	0.4609
2.7658	0.75	83000	2.7798	0.4610
2.7622	0.76	84000	2.7815	0.4612
2.7691	0.77	85000	2.7783	0.4612
2.7579	0.78	86000	2.7712	0.4619
2.7614	0.79	87000	2.7673	0.4625
2.7592	0.8	88000	2.7691	0.4623
2.7551	0.8	89000	2.7607	0.4634
2.7397	0.81	90000	2.7579	0.4637
2.7357	0.82	91000	2.7580	0.4636
2.7452	0.83	92000	2.7517	0.4643
2.7418	0.84	93000	2.7533	0.4641
2.7379	0.85	94000	2.7481	0.4647
2.7308	0.86	95000	2.7460	0.4654
2.727	0.87	96000	2.7408	0.4655
2.7282	0.88	97000	2.7351	0.4664
2.7133	0.89	98000	2.7301	0.4669
2.7136	0.9	99000	2.7251	0.4673
2.7108	0.9	100000	2.7208	0.4679
2.7051	0.91	101000	2.7192	0.4681
2.7013	0.92	102000	2.7151	0.4687
2.6996	0.93	103000	2.7129	0.4689
2.6898	0.94	104000	2.7084	0.4694
2.688	0.95	105000	2.7053	0.4697
2.6855	0.96	106000	2.7018	0.4701
2.6852	0.97	107000	2.6989	0.4705
2.689	0.98	108000	2.6982	0.4705
2.6868	0.99	109000	2.6994	0.4707
2.6901	0.99	110000	2.7006	0.4707

Framework versions

Transformers 4.34.0
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.14.1