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20 hours ago •
explosion
explosion
Training transformer model goes from score 0.97 to ZERO
Discussed in https://github.com/explosion/spaCy/discussions/12301
Originally posted by mbrunecky February 18, 2023 I am training NER using transformer model. On one of my data sets, during epoch 2, the score reaches 0.97 and then (after a huge loss) drops to ZERO, where it stays until the process dies with an out-of-memory error.
What I should I be looking for as the reason for this behavior?
02/18-02:52:32.282 ============================= Training pipeline =============================[0m
02/18-02:52:32.282 [i] Pipeline: ['transformer', 'ner', 'doc_cleaner']
02/18-02:52:32.282 [i] Initial learn rate: 0.0
02/18-02:52:32.282 E # LOSS TRANS... LOSS NER ENTS_F ENTS_P ENTS_R SCORE
02/18-02:52:32.282 --- ------ ------------- -------- ------ ------ ------ ------
02/18-02:53:26.942 0 0 741.03 842.20 0.83 0.44 6.68 0.03
02/18-03:00:53.389 0 800 35387.67 131378.27 92.45 91.63 93.28 0.93
02/18-03:08:21.388 0 1600 846.64 93264.55 92.85 92.78 92.91 0.93
02/18-03:15:56.981 0 2400 5107.06 68810.17 94.86 95.75 93.99 0.95
02/18-03:23:40.199 0 3200 23586.03 35748.45 95.69 96.39 95.01 0.96
02/18-03:31:42.270 0 4000 3324.74 10904.08 95.27 95.47 95.08 0.95
02/18-03:40:10.199 1 4800 69579.98 3293.41 95.71 95.29 96.13 0.96
02/18-03:49:08.304 1 5600 15203.48 1351.42 96.14 96.01 96.27 0.96
02/18-03:58:35.240 1 6400 5012.19 1022.37 96.19 96.33 96.06 0.96
02/18-04:08:44.572 1 7200 2621.33 943.09 95.85 95.30 96.40 0.96
02/18-04:19:21.697 1 8000 2262.92 829.70 96.75 97.13 96.37 0.97
02/18-04:31:10.735 1 8800 10229.21 982.74 95.90 97.48 94.37 0.96
02/18-04:43:10.557 2 9600 29553.29 1354.11 96.03 95.29 96.78 0.96
02/18-04:56:31.975 2 10400 3775.07 824.47 96.61 97.12 96.10 0.97
02/18-05:10:22.435 2 11200 2795971.49 12601.45 0.00 0.00 0.00 0.00
02/18-05:25:14.185 2 12000 513981.72 22502.53 0.00 0.00 0.00 0.00
02/18-05:40:56.915 2 12800 40347.06 18249.37 0.00 0.00 0.00 0.00
02/18-05:59:26.751 2 13600 34795.68 18328.94 0.00 0.00 0.00 0.00
02/18-06:18:05.600 3 14400 32507.22 19082.38 0.00 0.00 0.00 0.00
02/18-06:37:15.405 3 15200 27791.56 18447.91 0.00 0.00 0.00 0.00
02/18-06:57:16.382 3 16000 25837.16 18390.90 0.00 0.00 0.00 0.00
02/18-06:57:26.490 [+] Saved pipeline to output directory
02/18-06:59:28.779 Invoked train_run_004:: process finished, exit value=-1073741571 (0xc00000fd)
Configuration:
[paths]
train = "L:\\training\\CA\\PLACER\\FEB23\\DMOD\\train"
dev = "L:\\training\\CA\\PLACER\\FEB23\\DMOD\\tval"
vectors = null
init_tok2vec = null
[system]
gpu_allocator = "pytorch"
seed = 0
[nlp]
lang = "en"
pipeline = ["transformer","ner","doc_cleaner"]
batch_size = 80
disabled = []
after_creation = null
after_pipeline_creation = null
tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
[nlp.before_creation]
@callbacks = "adjust_stop_words"
add_stop_words = []
rem_stop_words = ["amount","and","as","at","between","by","eight","eleven","each","except","fifteen","fifty","first","five","for","formerly","forty","four","hereby","herein","nine","of","six","sixty","ten","third","three","to","twelve","twenty","two"]
debug = true
[components]
[components.doc_cleaner]
factory = "doc_cleaner"
silent = true
[components.doc_cleaner.attrs]
tensor = null
_.trf_data = null
[components.ner]
factory = "ner"
incorrect_spans_key = null
moves = null
scorer = {"@scorers":"spacy.ner_scorer.v1"}
update_with_oracle_cut_size = 128
[components.ner.model]
@architectures = "spacy.TransitionBasedParser.v2"
state_type = "ner"
extra_state_tokens = false
hidden_width = 80
maxout_pieces = 2
use_upper = false
nO = null
[components.ner.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
pooling = {"@layers":"reduce_mean.v1"}
upstream = "*"
[components.transformer]
factory = "transformer"
max_batch_items = 2048
set_extra_annotations = {"@annotation_setters":"spacy-transformers.null_annotation_setter.v1"}
[components.transformer.model]
@architectures = "spacy-transformers.TransformerModel.v3"
name = "roberta-base"
mixed_precision = true
[components.transformer.model.get_spans]
@span_getters = "spacy-transformers.strided_spans.v1"
window = 128
stride = 80
[components.transformer.model.grad_scaler_config]
[components.transformer.model.tokenizer_config]
use_fast = true
[components.transformer.model.transformer_config]
[corpora]
[corpora.dev]
@readers = "spacy.Corpus.v1"
path = ${paths.dev}
max_length = 0
gold_preproc = true
limit = 0
augmenter = null
[corpora.train]
@readers = "spacy.Corpus.v1"
path = ${paths.train}
max_length = 0
gold_preproc = true
limit = 0
augmenter = null
[training]
accumulate_gradient = 3
dev_corpus = "corpora.dev"
train_corpus = "corpora.train"
seed = ${system.seed}
gpu_allocator = ${system.gpu_allocator}
dropout = 0.1
patience = 8000
max_epochs = 0
max_steps = 32000
eval_frequency = 800
frozen_components = []
before_to_disk = null
annotating_components = []
[training.batcher]
@batchers = "spacy.batch_by_padded.v1"
discard_oversize = true
size = 1536
buffer = 256
get_length = null
[training.logger]
@loggers = "spacy.ConsoleLogger.v1"
progress_bar = false
[training.optimizer]
@optimizers = "Adam.v1"
beta1 = 0.9
beta2 = 0.999
L2_is_weight_decay = true
L2 = 0.01
grad_clip = 1.0
use_averages = false
eps = 0.00000001
[training.optimizer.learn_rate]
@schedules = "warmup_linear.v1"
warmup_steps = 250
total_steps = 32000
initial_rate = 0.00005
[training.score_weights]
ents_f = 0.5
ents_p = 0.2
ents_r = 0.3
ents_per_type = null
[pretraining]
[initialize]
vectors = null
init_tok2vec = null
vocab_data = null
lookups = null
before_init = null
after_init = null
[initialize.components]
[initialize.tokenizer]
```</div>
