add experience
Browse files- README.md +59 -0
- runs.json +196 -0
- tensorboard/1657707611.2962544/events.out.tfevents.1657707611.ip-10-2-64-206.ec2.internal.1.1 +3 -0
- tensorboard/1657707611.2976747/events.out.tfevents.1657707611.ip-10-2-64-206.ec2.internal.1.2 +3 -0
- tensorboard/events.out.tfevents.1657707611.ip-10-2-64-206.ec2.internal.1.0 +3 -0
README.md
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---
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pipeline_tag: token-classification
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datasets:
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- conll2003
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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tags:
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- distilbert
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---
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**task**: `token-classification`
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**Backend:** `sagemaker-training`
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**Backend args:** `{'instance_type': 'ml.g4dn.2xlarge', 'supported_instructions': None}`
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**Number of evaluation samples:** `All dataset`
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Fixed parameters:
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* **model_name_or_path**: `elastic/distilbert-base-uncased-finetuned-conll03-english`
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* **dataset**:
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* **path**: `conll2003`
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* **eval_split**: `validation`
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* **data_keys**: `{'primary': 'tokens'}`
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* **ref_keys**: `['ner_tags']`
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* **calibration_split**: `train`
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* **quantization_approach**: `static`
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* **operators_to_quantize**: `['Add', 'MatMul']`
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* **per_channel**: `False`
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* **calibration**:
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* **method**: `minmax`
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* **num_calibration_samples**: `100`
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* **framework**: `onnxruntime`
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* **framework_args**:
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* **opset**: `11`
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* **optimization_level**: `1`
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* **aware_training**: `False`
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Benchmarked parameters:
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* **node_exclusion**: `[]`, `['layernorm', 'gelu', 'residual', 'gather', 'softmax']`
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# Evaluation
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## Non-time metrics
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| node_exclusion | | precision (original) | precision (optimized) | | recall (original) | recall (optimized) | | f1 (original) | f1 (optimized) | | accuracy (original) | accuracy (optimized) |
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| :------------------------------------------------------: | :-: | :------------------: | :-------------------: | :-: | :---------------: | :----------------: | :-: | :-----------: | :------------: | :-: | :-----------------: | :------------------: |
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| `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 0.936 | 0.904 | \| | 0.944 | 0.921 | \| | 0.940 | 0.912 | \| | 0.988 | 0.984 |
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| `[]` | \| | 0.936 | 0.065 | \| | 0.944 | 0.243 | \| | 0.940 | 0.103 | \| | 0.988 | 0.357 |
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## Time metrics
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Time benchmarks were run for 15 seconds per config.
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Below, time metrics for batch size = 4, input length = 64.
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| node_exclusion | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
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| :------------------------------------------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
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| `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 120.53 | 46.41 | \| | 8.33 | 21.60 |
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| `[]` | \| | 119.97 | 59.50 | \| | 8.40 | 16.87 |
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runs.json
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[
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{
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"model_name_or_path": "elastic/distilbert-base-uncased-finetuned-conll03-english",
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"task": "token-classification",
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5 |
+
"task_args": null,
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6 |
+
"dataset": {
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"path": "conll2003",
|
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+
"eval_split": "validation",
|
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"data_keys": {
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"primary": "tokens",
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"secondary": null
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+
},
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+
"ref_keys": [
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"ner_tags"
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+
],
|
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"name": null,
|
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"calibration_split": "train"
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+
},
|
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+
"quantization_approach": "static",
|
20 |
+
"operators_to_quantize": [
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"Add",
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22 |
+
"MatMul"
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+
],
|
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+
"node_exclusion": [
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+
"layernorm",
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+
"gelu",
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+
"residual",
|
28 |
+
"gather",
|
29 |
+
"softmax"
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30 |
+
],
|
31 |
+
"aware_training": false,
|
32 |
+
"per_channel": false,
|
33 |
+
"calibration": {
|
34 |
+
"method": "minmax",
|
35 |
+
"num_calibration_samples": 100,
|
36 |
+
"calibration_histogram_percentile": null,
|
37 |
+
"calibration_moving_average": null,
|
38 |
+
"calibration_moving_average_constant": null
|
39 |
+
},
|
40 |
+
"framework": "onnxruntime",
|
41 |
+
"framework_args": {
|
42 |
+
"opset": 11,
|
43 |
+
"optimization_level": 1
|
44 |
+
},
|
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+
"hardware": "Architecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nByte Order: Little Endian\nAddress sizes: 46 bits physical, 48 bits virtual\nCPU(s): 8\nOn-line CPU(s) list: 0-7\nThread(s) per core: 2\nCore(s) per socket: 4\nSocket(s): 1\nNUMA node(s): 1\nVendor ID: GenuineIntel\nCPU family: 6\nModel: 85\nModel name: Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz\nStepping: 7\nCPU MHz: 3099.967\nBogoMIPS: 4999.99\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 128 KiB\nL1i cache: 128 KiB\nL2 cache: 4 MiB\nL3 cache: 35.8 MiB\nNUMA node0 CPU(s): 0-7\nVulnerability Itlb multihit: KVM: Vulnerable\nVulnerability L1tf: Mitigation; PTE Inversion\nVulnerability Mds: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown\nVulnerability Meltdown: Mitigation; PTI\nVulnerability Spec store bypass: Vulnerable\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines, STIBP disabled, RSB filling\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single pti fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves ida arat pku ospke avx512_vnni\n",
|
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+
"versions": {
|
47 |
+
"transformers": "4.20.1",
|
48 |
+
"optimum": "1.2.3.dev0",
|
49 |
+
"optimum_hash": "5c9af4e5f93c7e9bd523563230732b49603dc4d7"
|
50 |
+
},
|
51 |
+
"evaluation": {
|
52 |
+
"time": [
|
53 |
+
{
|
54 |
+
"batch_size": 4,
|
55 |
+
"input_length": 64,
|
56 |
+
"baseline": {
|
57 |
+
"nb_forwards": 125,
|
58 |
+
"throughput": 8.33,
|
59 |
+
"latency_mean": 120.533118016,
|
60 |
+
"latency_std": 0.8359515558530887,
|
61 |
+
"latency_50": 120.363805,
|
62 |
+
"latency_90": 121.5990676,
|
63 |
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"latency_95": 121.9226278,
|
64 |
+
"latency_99": 123.02248340000001,
|
65 |
+
"latency_999": 123.981240332
|
66 |
+
},
|
67 |
+
"optimized": {
|
68 |
+
"nb_forwards": 324,
|
69 |
+
"throughput": 21.6,
|
70 |
+
"latency_mean": 46.41411868827161,
|
71 |
+
"latency_std": 1.7434868124538356,
|
72 |
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"latency_50": 46.258802,
|
73 |
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"latency_90": 48.954860200000006,
|
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"latency_95": 49.46207725,
|
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"latency_99": 50.02644861,
|
76 |
+
"latency_999": 50.308738387000005
|
77 |
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}
|
78 |
+
}
|
79 |
+
],
|
80 |
+
"others": {
|
81 |
+
"baseline": {
|
82 |
+
"precision": 0.9358012339503085,
|
83 |
+
"recall": 0.9444631437226523,
|
84 |
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"f1": 0.9401122372057961,
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"accuracy": 0.9882013940267124
|
86 |
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},
|
87 |
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"optimized": {
|
88 |
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"precision": 0.9038969616908851,
|
89 |
+
"recall": 0.9212386401884888,
|
90 |
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"f1": 0.912485414235706,
|
91 |
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"accuracy": 0.9842295860753086
|
92 |
+
}
|
93 |
+
}
|
94 |
+
},
|
95 |
+
"max_eval_samples": null,
|
96 |
+
"time_benchmark_args": {
|
97 |
+
"duration": 15,
|
98 |
+
"warmup_runs": 5
|
99 |
+
},
|
100 |
+
"model_type": "distilbert"
|
101 |
+
},
|
102 |
+
{
|
103 |
+
"model_name_or_path": "elastic/distilbert-base-uncased-finetuned-conll03-english",
|
104 |
+
"task": "token-classification",
|
105 |
+
"task_args": null,
|
106 |
+
"dataset": {
|
107 |
+
"path": "conll2003",
|
108 |
+
"eval_split": "validation",
|
109 |
+
"data_keys": {
|
110 |
+
"primary": "tokens",
|
111 |
+
"secondary": null
|
112 |
+
},
|
113 |
+
"ref_keys": [
|
114 |
+
"ner_tags"
|
115 |
+
],
|
116 |
+
"name": null,
|
117 |
+
"calibration_split": "train"
|
118 |
+
},
|
119 |
+
"quantization_approach": "static",
|
120 |
+
"operators_to_quantize": [
|
121 |
+
"Add",
|
122 |
+
"MatMul"
|
123 |
+
],
|
124 |
+
"node_exclusion": [],
|
125 |
+
"aware_training": false,
|
126 |
+
"per_channel": false,
|
127 |
+
"calibration": {
|
128 |
+
"method": "minmax",
|
129 |
+
"num_calibration_samples": 100,
|
130 |
+
"calibration_histogram_percentile": null,
|
131 |
+
"calibration_moving_average": null,
|
132 |
+
"calibration_moving_average_constant": null
|
133 |
+
},
|
134 |
+
"framework": "onnxruntime",
|
135 |
+
"framework_args": {
|
136 |
+
"opset": 11,
|
137 |
+
"optimization_level": 1
|
138 |
+
},
|
139 |
+
"hardware": "Architecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nByte Order: Little Endian\nAddress sizes: 46 bits physical, 48 bits virtual\nCPU(s): 8\nOn-line CPU(s) list: 0-7\nThread(s) per core: 2\nCore(s) per socket: 4\nSocket(s): 1\nNUMA node(s): 1\nVendor ID: GenuineIntel\nCPU family: 6\nModel: 85\nModel name: Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz\nStepping: 7\nCPU MHz: 3100.910\nBogoMIPS: 4999.99\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 128 KiB\nL1i cache: 128 KiB\nL2 cache: 4 MiB\nL3 cache: 35.8 MiB\nNUMA node0 CPU(s): 0-7\nVulnerability Itlb multihit: KVM: Vulnerable\nVulnerability L1tf: Mitigation; PTE Inversion\nVulnerability Mds: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown\nVulnerability Meltdown: Mitigation; PTI\nVulnerability Spec store bypass: Vulnerable\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines, STIBP disabled, RSB filling\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single pti fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves ida arat pku ospke avx512_vnni\n",
|
140 |
+
"versions": {
|
141 |
+
"transformers": "4.20.1",
|
142 |
+
"optimum": "1.2.3.dev0",
|
143 |
+
"optimum_hash": "5c9af4e5f93c7e9bd523563230732b49603dc4d7"
|
144 |
+
},
|
145 |
+
"evaluation": {
|
146 |
+
"time": [
|
147 |
+
{
|
148 |
+
"batch_size": 4,
|
149 |
+
"input_length": 64,
|
150 |
+
"baseline": {
|
151 |
+
"nb_forwards": 126,
|
152 |
+
"throughput": 8.4,
|
153 |
+
"latency_mean": 119.97364652380952,
|
154 |
+
"latency_std": 1.1115995809677575,
|
155 |
+
"latency_50": 119.8484215,
|
156 |
+
"latency_90": 121.1081755,
|
157 |
+
"latency_95": 121.99861425,
|
158 |
+
"latency_99": 122.6797695,
|
159 |
+
"latency_999": 124.28595525
|
160 |
+
},
|
161 |
+
"optimized": {
|
162 |
+
"nb_forwards": 253,
|
163 |
+
"throughput": 16.87,
|
164 |
+
"latency_mean": 59.49775931225296,
|
165 |
+
"latency_std": 3.518559570257517,
|
166 |
+
"latency_50": 58.504581,
|
167 |
+
"latency_90": 64.1940764,
|
168 |
+
"latency_95": 66.06759079999999,
|
169 |
+
"latency_99": 68.27611223999999,
|
170 |
+
"latency_999": 69.44462915599999
|
171 |
+
}
|
172 |
+
}
|
173 |
+
],
|
174 |
+
"others": {
|
175 |
+
"baseline": {
|
176 |
+
"precision": 0.9358012339503085,
|
177 |
+
"recall": 0.9444631437226523,
|
178 |
+
"f1": 0.9401122372057961,
|
179 |
+
"accuracy": 0.9882013940267124
|
180 |
+
},
|
181 |
+
"optimized": {
|
182 |
+
"precision": 0.06543578604398588,
|
183 |
+
"recall": 0.24335240659710536,
|
184 |
+
"f1": 0.10313837375178317,
|
185 |
+
"accuracy": 0.35697597445582335
|
186 |
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}
|
187 |
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|
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|
189 |
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"max_eval_samples": null,
|
190 |
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"time_benchmark_args": {
|
191 |
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"duration": 15,
|
192 |
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"warmup_runs": 5
|
193 |
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},
|
194 |
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"model_type": "distilbert"
|
195 |
+
}
|
196 |
+
]
|
tensorboard/1657707611.2962544/events.out.tfevents.1657707611.ip-10-2-64-206.ec2.internal.1.1
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version https://git-lfs.github.com/spec/v1
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size 696
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tensorboard/1657707611.2976747/events.out.tfevents.1657707611.ip-10-2-64-206.ec2.internal.1.2
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:38bb5da1b600d8842d3d85a2b2104775dd0f3b1beddbfd60fe8de0f5c5522440
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size 644
|
tensorboard/events.out.tfevents.1657707611.ip-10-2-64-206.ec2.internal.1.0
ADDED
@@ -0,0 +1,3 @@
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|
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version https://git-lfs.github.com/spec/v1
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size 40
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