Instructions to use demdecuong/stroke_sup_simcse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use demdecuong/stroke_sup_simcse with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="demdecuong/stroke_sup_simcse")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("demdecuong/stroke_sup_simcse") model = AutoModel.from_pretrained("demdecuong/stroke_sup_simcse", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 3,380 Bytes
c2918b6 610888d c2918b6 610888d c2918b6 610888d c2918b6 610888d c2918b6 610888d c2918b6 610888d c2918b6 610888d c2918b6 610888d c2918b6 610888d c2918b6 610888d c2918b6 610888d c2918b6 610888d c2918b6 610888d c2918b6 610888d c2918b6 610888d c2918b6 610888d c2918b6 610888d c2918b6 610888d c2918b6 610888d c2918b6 610888d c2918b6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 | {
"best_metric": 0.8608760424983216,
"best_model_checkpoint": "result/my-sup-simcse-bert-base-uncased",
"epoch": 10.0,
"global_step": 1660,
"is_hyper_param_search": false,
"is_local_process_zero": true,
"is_world_process_zero": true,
"log_history": [
{
"epoch": 0.75,
"eval_avg_sts": 0.8315040583627913,
"eval_sickr_spearman": 0.8021320742272611,
"eval_stsb_spearman": 0.8608760424983216,
"step": 125
},
{
"epoch": 1.51,
"eval_avg_sts": 0.8276682684969617,
"eval_sickr_spearman": 0.8024215096445272,
"eval_stsb_spearman": 0.8529150273493963,
"step": 250
},
{
"epoch": 2.26,
"eval_avg_sts": 0.8292414115876203,
"eval_sickr_spearman": 0.8035029298934062,
"eval_stsb_spearman": 0.8549798932818345,
"step": 375
},
{
"epoch": 3.01,
"learning_rate": 3.4939759036144585e-05,
"loss": 0.1927,
"step": 500
},
{
"epoch": 3.01,
"eval_avg_sts": 0.8242638816167347,
"eval_sickr_spearman": 0.8022541692871167,
"eval_stsb_spearman": 0.8462735939463528,
"step": 500
},
{
"epoch": 3.77,
"eval_avg_sts": 0.8233283779153091,
"eval_sickr_spearman": 0.7945779827485818,
"eval_stsb_spearman": 0.8520787730820363,
"step": 625
},
{
"epoch": 4.52,
"eval_avg_sts": 0.8215947619403889,
"eval_sickr_spearman": 0.7907777620027984,
"eval_stsb_spearman": 0.8524117618779796,
"step": 750
},
{
"epoch": 5.27,
"eval_avg_sts": 0.8227357575888109,
"eval_sickr_spearman": 0.7947862936355189,
"eval_stsb_spearman": 0.8506852215421029,
"step": 875
},
{
"epoch": 6.02,
"learning_rate": 1.9879518072289157e-05,
"loss": 0.0995,
"step": 1000
},
{
"epoch": 6.02,
"eval_avg_sts": 0.8234624613863508,
"eval_sickr_spearman": 0.7961339983107709,
"eval_stsb_spearman": 0.8507909244619307,
"step": 1000
},
{
"epoch": 6.78,
"eval_avg_sts": 0.8199047038936794,
"eval_sickr_spearman": 0.7917320919572722,
"eval_stsb_spearman": 0.8480773158300866,
"step": 1125
},
{
"epoch": 7.53,
"eval_avg_sts": 0.8194132158921612,
"eval_sickr_spearman": 0.7884059381826829,
"eval_stsb_spearman": 0.8504204936016395,
"step": 1250
},
{
"epoch": 8.28,
"eval_avg_sts": 0.8188285236637569,
"eval_sickr_spearman": 0.7876833102615466,
"eval_stsb_spearman": 0.8499737370659672,
"step": 1375
},
{
"epoch": 9.04,
"learning_rate": 4.819277108433735e-06,
"loss": 0.0874,
"step": 1500
},
{
"epoch": 9.04,
"eval_avg_sts": 0.8193720271717073,
"eval_sickr_spearman": 0.7877823023616106,
"eval_stsb_spearman": 0.850961751981804,
"step": 1500
},
{
"epoch": 9.79,
"eval_avg_sts": 0.8188424049556793,
"eval_sickr_spearman": 0.7873486775778272,
"eval_stsb_spearman": 0.8503361323335314,
"step": 1625
},
{
"epoch": 10.0,
"step": 1660,
"train_runtime": 4081.7638,
"train_samples_per_second": 0.407
}
],
"max_steps": 1660,
"num_train_epochs": 10,
"total_flos": 26836143897968640,
"trial_name": null,
"trial_params": null
}
|