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log_croatian_10.out
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DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): huggingface.co:443
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loading configuration file https://huggingface.co/xlm-roberta-base/resolve/main/config.json from cache at /home/mrajnovic/.cache/huggingface/transformers/87683eb92ea383b0475fecf99970e950a03c9ff5e51648d6eee56fb754612465.dfaaaedc7c1c475302398f09706cbb21e23951b73c6e2b3162c1c8a99bb3b62a
Model config XLMRobertaConfig {
"_name_or_path": "xlm-roberta-base",
"architectures": [
"XLMRobertaForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "xlm-roberta",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"output_past": true,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"transformers_version": "4.18.0",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 250002
}
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PyTorch: setting up devices
The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): huggingface.co:443
DEBUG:urllib3.connectionpool:https://huggingface.co:443 "HEAD /xlm-roberta-base/resolve/main/config.json HTTP/1.1" 200 0
loading configuration file https://huggingface.co/xlm-roberta-base/resolve/main/config.json from cache at /home/mrajnovic/.cache/huggingface/transformers/87683eb92ea383b0475fecf99970e950a03c9ff5e51648d6eee56fb754612465.dfaaaedc7c1c475302398f09706cbb21e23951b73c6e2b3162c1c8a99bb3b62a
Model config XLMRobertaConfig {
"architectures": [
"XLMRobertaForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2",
"3": "LABEL_3",
"4": "LABEL_4",
"5": "LABEL_5",
"6": "LABEL_6",
"7": "LABEL_7",
"8": "LABEL_8",
"9": "LABEL_9",
"10": "LABEL_10",
"11": "LABEL_11",
"12": "LABEL_12",
"13": "LABEL_13",
"14": "LABEL_14",
"15": "LABEL_15",
"16": "LABEL_16",
"17": "LABEL_17",
"18": "LABEL_18",
"19": "LABEL_19",
"20": "LABEL_20"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_10": 10,
"LABEL_11": 11,
"LABEL_12": 12,
"LABEL_13": 13,
"LABEL_14": 14,
"LABEL_15": 15,
"LABEL_16": 16,
"LABEL_17": 17,
"LABEL_18": 18,
"LABEL_19": 19,
"LABEL_2": 2,
"LABEL_20": 20,
"LABEL_3": 3,
"LABEL_4": 4,
"LABEL_5": 5,
"LABEL_6": 6,
"LABEL_7": 7,
"LABEL_8": 8,
"LABEL_9": 9
},
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "xlm-roberta",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"output_past": true,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"problem_type": "multi_label_classification",
"transformers_version": "4.18.0",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 250002
}
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loading weights file https://huggingface.co/xlm-roberta-base/resolve/main/pytorch_model.bin from cache at /home/mrajnovic/.cache/huggingface/transformers/97d0ea09f8074264957d062ec20ccb79af7b917d091add8261b26874daf51b5d.f42212747c1c27fcebaa0a89e2a83c38c6d3d4340f21922f892b88d882146ac2
Some weights of the model checkpoint at xlm-roberta-base were not used when initializing XLMRobertaForSequenceClassification: ['lm_head.bias', 'lm_head.dense.weight', 'roberta.pooler.dense.bias', 'lm_head.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.decoder.weight', 'roberta.pooler.dense.weight', 'lm_head.layer_norm.bias']
- This IS expected if you are initializing XLMRobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing XLMRobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of XLMRobertaForSequenceClassification were not initialized from the model checkpoint at xlm-roberta-base and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.dense.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
/home/mrajnovic/miniconda3/envs/tar/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
warnings.warn(
***** Running training *****
Num examples = 7944
Num Epochs = 10
Instantaneous batch size per device = 8
Total train batch size (w. parallel, distributed & accumulation) = 8
Gradient Accumulation steps = 1
Total optimization steps = 9930
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Running Evaluation *****
Num examples = 2500
Batch size = 16
{'loss': 0.6958, 'learning_rate': 1.0070493454179256e-06, 'epoch': 0.1}
{'loss': 0.5904, 'learning_rate': 2.0140986908358513e-06, 'epoch': 0.2}
{'loss': 0.4915, 'learning_rate': 3.0211480362537765e-06, 'epoch': 0.3}
{'loss': 0.4464, 'learning_rate': 4.028197381671703e-06, 'epoch': 0.4}
{'loss': 0.42, 'learning_rate': 5.035246727089627e-06, 'epoch': 0.5}
{'loss': 0.4067, 'learning_rate': 6.042296072507553e-06, 'epoch': 0.6}
{'loss': 0.3894, 'learning_rate': 7.0493454179254795e-06, 'epoch': 0.7}
{'loss': 0.381, 'learning_rate': 8.056394763343405e-06, 'epoch': 0.81}
{'loss': 0.3617, 'learning_rate': 9.06344410876133e-06, 'epoch': 0.91}
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[ASaving model checkpoint to ./results/croatian_10/checkpoint-993
Configuration saved in ./results/croatian_10/checkpoint-993/config.json
Model weights saved in ./results/croatian_10/checkpoint-993/pytorch_model.bin
{'eval_loss': 0.3264726996421814, 'eval_f1': 0.47867945165945164, 'eval_runtime': 31.7752, 'eval_samples_per_second': 78.678, 'eval_steps_per_second': 4.941, 'epoch': 1.0}
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[11:50<45:10, 2.93it/s] 20%|█▉ | 1977/9930 [11:51<45:05, 2.94it/s] 20%|█▉ | 1978/9930 [11:51<45:09, 2.93it/s] 20%|█▉ | 1979/9930 [11:51<45:10, 2.93it/s] 20%|█▉ | 1980/9930 [11:52<45:04, 2.94it/s] 20%|█▉ | 1981/9930 [11:52<45:02, 2.94it/s] 20%|█▉ | 1982/9930 [11:52<45:11, 2.93it/s] 20%|█▉ | 1983/9930 [11:53<45:20, 2.92it/s] 20%|█▉ | 1984/9930 [11:53<45:14, 2.93it/s] 20%|█▉ | 1985/9930 [11:53<45:09, 2.93it/s] 20%|██ | 1986/9930 [11:54<45:04, 2.94it/s]***** Running Evaluation *****
Num examples = 2500
Batch size = 16
{'loss': 0.343, 'learning_rate': 9.992245720040283e-06, 'epoch': 1.01}
{'loss': 0.3227, 'learning_rate': 9.88147029204431e-06, 'epoch': 1.11}
{'loss': 0.3074, 'learning_rate': 9.77069486404834e-06, 'epoch': 1.21}
{'loss': 0.3001, 'learning_rate': 9.659919436052367e-06, 'epoch': 1.31}
{'loss': 0.293, 'learning_rate': 9.549144008056396e-06, 'epoch': 1.41}
{'loss': 0.2913, 'learning_rate': 9.438368580060424e-06, 'epoch': 1.51}
{'loss': 0.2816, 'learning_rate': 9.327593152064452e-06, 'epoch': 1.61}
{'loss': 0.2789, 'learning_rate': 9.216817724068479e-06, 'epoch': 1.71}
{'loss': 0.2568, 'learning_rate': 9.106042296072508e-06, 'epoch': 1.81}
{'loss': 0.2622, 'learning_rate': 8.995266868076536e-06, 'epoch': 1.91}
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[ASaving model checkpoint to ./results/croatian_10/checkpoint-1986
Configuration saved in ./results/croatian_10/checkpoint-1986/config.json
Model weights saved in ./results/croatian_10/checkpoint-1986/pytorch_model.bin
{'eval_loss': 0.24398496747016907, 'eval_f1': 0.6903250793650794, 'eval_runtime': 31.9525, 'eval_samples_per_second': 78.241, 'eval_steps_per_second': 4.914, 'epoch': 2.0}
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| 2953/9930 [18:01<39:37, 2.93it/s] 30%|██▉ | 2954/9930 [18:02<39:36, 2.94it/s] 30%|██▉ | 2955/9930 [18:02<39:43, 2.93it/s] 30%|██▉ | 2956/9930 [18:02<39:38, 2.93it/s] 30%|██▉ | 2957/9930 [18:03<39:36, 2.93it/s] 30%|██▉ | 2958/9930 [18:03<39:35, 2.94it/s] 30%|██▉ | 2959/9930 [18:03<39:38, 2.93it/s] 30%|██▉ | 2960/9930 [18:04<39:36, 2.93it/s] 30%|██▉ | 2961/9930 [18:04<39:43, 2.92it/s] 30%|██▉ | 2962/9930 [18:04<39:41, 2.93it/s] 30%|██▉ | 2963/9930 [18:05<39:40, 2.93it/s] 30%|██▉ | 2964/9930 [18:05<39:35, 2.93it/s] 30%|██▉ | 2965/9930 [18:05<39:33, 2.93it/s] 30%|██▉ | 2966/9930 [18:06<39:32, 2.93it/s] 30%|██▉ | 2967/9930 [18:06<39:33, 2.93it/s] 30%|██▉ | 2968/9930 [18:06<39:35, 2.93it/s] 30%|██▉ | 2969/9930 [18:07<39:34, 2.93it/s] 30%|██▉ | 2970/9930 [18:07<39:32, 2.93it/s] 30%|██▉ | 2971/9930 [18:08<39:38, 2.93it/s] 30%|██▉ | 2972/9930 [18:08<39:37, 2.93it/s] 30%|██▉ | 2973/9930 [18:08<39:47, 2.91it/s] 30%|██▉ | 2974/9930 [18:09<39:45, 2.92it/s] 30%|██▉ | 2975/9930 [18:09<39:50, 2.91it/s] 30%|██▉ | 2976/9930 [18:09<39:52, 2.91it/s] 30%|██▉ | 2977/9930 [18:10<39:48, 2.91it/s] 30%|██▉ | 2978/9930 [18:10<39:40, 2.92it/s] 30%|███ | 2979/9930 [18:10<39:34, 2.93it/s]***** Running Evaluation *****
Num examples = 2500
Batch size = 16
{'loss': 0.2549, 'learning_rate': 8.884491440080565e-06, 'epoch': 2.01}
{'loss': 0.2481, 'learning_rate': 8.773716012084593e-06, 'epoch': 2.11}
{'loss': 0.2482, 'learning_rate': 8.66294058408862e-06, 'epoch': 2.22}
{'loss': 0.2345, 'learning_rate': 8.55216515609265e-06, 'epoch': 2.32}
{'loss': 0.2398, 'learning_rate': 8.441389728096677e-06, 'epoch': 2.42}
{'loss': 0.2393, 'learning_rate': 8.330614300100705e-06, 'epoch': 2.52}
{'loss': 0.2337, 'learning_rate': 8.219838872104734e-06, 'epoch': 2.62}
{'loss': 0.2313, 'learning_rate': 8.109063444108762e-06, 'epoch': 2.72}
{'loss': 0.2268, 'learning_rate': 7.99828801611279e-06, 'epoch': 2.82}
{'loss': 0.228, 'learning_rate': 7.887512588116818e-06, 'epoch': 2.92}
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[ASaving model checkpoint to ./results/croatian_10/checkpoint-2979
Configuration saved in ./results/croatian_10/checkpoint-2979/config.json
Model weights saved in ./results/croatian_10/checkpoint-2979/pytorch_model.bin
{'eval_loss': 0.2199530452489853, 'eval_f1': 0.7364263492063492, 'eval_runtime': 32.2041, 'eval_samples_per_second': 77.63, 'eval_steps_per_second': 4.875, 'epoch': 3.0}
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[24:24<33:48, 2.94it/s] 40%|███▉ | 3964/9930 [24:24<33:49, 2.94it/s] 40%|███▉ | 3965/9930 [24:24<33:50, 2.94it/s] 40%|███▉ | 3966/9930 [24:25<33:52, 2.93it/s] 40%|███▉ | 3967/9930 [24:25<33:51, 2.93it/s] 40%|███▉ | 3968/9930 [24:25<33:50, 2.94it/s] 40%|███▉ | 3969/9930 [24:26<33:49, 2.94it/s] 40%|███▉ | 3970/9930 [24:26<33:53, 2.93it/s] 40%|███▉ | 3971/9930 [24:26<33:53, 2.93it/s] 40%|████ | 3972/9930 [24:27<33:54, 2.93it/s]***** Running Evaluation *****
Num examples = 2500
Batch size = 16
{'loss': 0.2231, 'learning_rate': 7.776737160120848e-06, 'epoch': 3.02}
{'loss': 0.2154, 'learning_rate': 7.665961732124873e-06, 'epoch': 3.12}
{'loss': 0.2151, 'learning_rate': 7.555186304128902e-06, 'epoch': 3.22}
{'loss': 0.2116, 'learning_rate': 7.44441087613293e-06, 'epoch': 3.32}
{'loss': 0.2092, 'learning_rate': 7.333635448136959e-06, 'epoch': 3.42}
{'loss': 0.2054, 'learning_rate': 7.222860020140987e-06, 'epoch': 3.52}
{'loss': 0.2086, 'learning_rate': 7.112084592145015e-06, 'epoch': 3.63}
{'loss': 0.2061, 'learning_rate': 7.001309164149044e-06, 'epoch': 3.73}
{'loss': 0.2102, 'learning_rate': 6.8905337361530715e-06, 'epoch': 3.83}
{'loss': 0.2107, 'learning_rate': 6.779758308157101e-06, 'epoch': 3.93}
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[ASaving model checkpoint to ./results/croatian_10/checkpoint-3972
Configuration saved in ./results/croatian_10/checkpoint-3972/config.json
Model weights saved in ./results/croatian_10/checkpoint-3972/pytorch_model.bin
{'eval_loss': 0.20785652101039886, 'eval_f1': 0.744674862914863, 'eval_runtime': 31.9061, 'eval_samples_per_second': 78.355, 'eval_steps_per_second': 4.921, 'epoch': 4.0}
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| 4961/9930 [30:42<28:25, 2.91it/s] 50%|████▉ | 4962/9930 [30:42<28:23, 2.92it/s] 50%|████▉ | 4963/9930 [30:43<28:21, 2.92it/s] 50%|████▉ | 4964/9930 [30:43<28:23, 2.92it/s] 50%|█████ | 4965/9930 [30:43<28:20, 2.92it/s]***** Running Evaluation *****
Num examples = 2500
Batch size = 16
{'loss': 0.2, 'learning_rate': 6.6689828801611274e-06, 'epoch': 4.03}
{'loss': 0.1965, 'learning_rate': 6.558207452165156e-06, 'epoch': 4.13}
{'loss': 0.1876, 'learning_rate': 6.447432024169184e-06, 'epoch': 4.23}
{'loss': 0.1938, 'learning_rate': 6.336656596173212e-06, 'epoch': 4.33}
{'loss': 0.1865, 'learning_rate': 6.225881168177241e-06, 'epoch': 4.43}
{'loss': 0.1923, 'learning_rate': 6.115105740181269e-06, 'epoch': 4.53}
{'loss': 0.1961, 'learning_rate': 6.004330312185298e-06, 'epoch': 4.63}
{'loss': 0.1924, 'learning_rate': 5.893554884189325e-06, 'epoch': 4.73}
{'loss': 0.1901, 'learning_rate': 5.782779456193352e-06, 'epoch': 4.83}
{'loss': 0.1895, 'learning_rate': 5.6720040281973806e-06, 'epoch': 4.93}
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