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nateraw /bge-large-en-v1.5:9cf9f015
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=nateraw/bge-large-en-v1.5
or set up a project from scratch
Install Replicate’s Node.js client library:
npm install replicateSet the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:
import Replicate from "replicate";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});Run nateraw/bge-large-en-v1.5 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"nateraw/bge-large-en-v1.5:9cf9f015a9cb9c61d1a2610659cdac4a4ca222f2d3707a68517b18c198a9add1",
{
input: {
path: "https://replicate.delivery/pbxt/Je5WZ8zvLRFwGKANUdGLuc5xu599R2JF5I4RvxLpuTUg7ek5/samsum_as_list.txt",
texts: "",
batch_size: 32,
convert_to_numpy: true,
normalize_embeddings: true
}
}
);
console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:
pip install replicateSet the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:
import replicateRun nateraw/bge-large-en-v1.5 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"nateraw/bge-large-en-v1.5:9cf9f015a9cb9c61d1a2610659cdac4a4ca222f2d3707a68517b18c198a9add1",
input={
"path": "https://replicate.delivery/pbxt/Je5WZ8zvLRFwGKANUdGLuc5xu599R2JF5I4RvxLpuTUg7ek5/samsum_as_list.txt",
"texts": "",
"batch_size": 32,
"convert_to_numpy": True,
"normalize_embeddings": True
}
)
print(output)To learn more, take a look at the guide on getting started with Python.
Set the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run nateraw/bge-large-en-v1.5 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \
-H "Authorization: Bearer $REPLICATE_API_TOKEN" \
-H "Content-Type: application/json" \
-H "Prefer: wait" \
-d $'{
"version": "nateraw/bge-large-en-v1.5:9cf9f015a9cb9c61d1a2610659cdac4a4ca222f2d3707a68517b18c198a9add1",
"input": {
"path": "https://replicate.delivery/pbxt/Je5WZ8zvLRFwGKANUdGLuc5xu599R2JF5I4RvxLpuTUg7ek5/samsum_as_list.txt",
"texts": "",
"batch_size": 32,
"convert_to_numpy": true,
"normalize_embeddings": true
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Add a payment method to run this model.
Sign in with GitHub
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terms of service and privacy policy
Output
https://replicate.delivery/pbxt/6YAHCm6m5ZpoKBzqcT9seN98GRhlFhhqPbzFyuwP8FTRTh1IA/embeddings.npy
{
"completed_at": "2023-10-05T08:45:24.441733Z",
"created_at": "2023-10-05T08:42:41.627618Z",
"data_removed": false,
"error": null,
"id": "jxrcdk3bqc2j6tbjb4fc5nonmm",
"input": {
"path": "https://replicate.delivery/pbxt/Je5WZ8zvLRFwGKANUdGLuc5xu599R2JF5I4RvxLpuTUg7ek5/samsum_as_list.txt",
"texts": "",
"batch_size": 32,
"convert_to_numpy": true,
"normalize_embeddings": true
},
"logs": "Torch cuda available True\ntorch cuda.get_device_name(0) NVIDIA A40\nort.get_available_providers() ['TensorrtExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider']\nGenerating train split: 0 examples [00:00, ? examples/s]\nGenerating train split: 0 examples [00:00, ? examples/s]\nFailed to load as jsonl, trying as if it's a JSON list of strings\nParameter 'function'=<function Predictor.predict.<locals>.process at 0x7f88e310fca0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\nMap: 0%| | 0/14732 [00:00<?, ? examples/s]\nBatches: 0%| | 0/157 [00:00<?, ?it/s]\u001b[A\nBatches: 1%| | 1/157 [00:00<01:15, 2.06it/s]\u001b[A\nBatches: 1%|▏ | 2/157 [00:00<01:13, 2.10it/s]\u001b[A\nBatches: 2%|▏ | 3/157 [00:01<01:12, 2.12it/s]\u001b[A\nBatches: 3%|▎ | 4/157 [00:01<01:11, 2.13it/s]\u001b[A\nBatches: 3%|▎ | 5/157 [00:02<01:11, 2.13it/s]\u001b[A\nBatches: 4%|▍ | 6/157 [00:02<01:10, 2.14it/s]\u001b[A\nBatches: 4%|▍ | 7/157 [00:03<01:08, 2.19it/s]\u001b[A\nBatches: 5%|▌ | 8/157 [00:03<01:07, 2.22it/s]\u001b[A\nBatches: 6%|▌ | 9/157 [00:04<01:05, 2.27it/s]\u001b[A\nBatches: 6%|▋ | 10/157 [00:04<01:03, 2.30it/s]\u001b[A\nBatches: 7%|▋ | 11/157 [00:04<01:01, 2.38it/s]\u001b[A\nBatches: 8%|▊ | 12/157 [00:05<00:58, 2.49it/s]\u001b[A\nBatches: 8%|▊ | 13/157 [00:05<01:00, 2.37it/s]\u001b[A\nBatches: 9%|▉ | 14/157 [00:06<00:56, 2.52it/s]\u001b[A\nBatches: 10%|▉ | 15/157 [00:06<00:53, 2.63it/s]\u001b[A\nBatches: 10%|█ | 16/157 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135/157 [00:28<00:01, 11.11it/s]\u001b[A\nBatches: 87%|████████▋ | 137/157 [00:28<00:01, 10.98it/s]\u001b[A\nBatches: 89%|████████▊ | 139/157 [00:28<00:01, 11.27it/s]\u001b[A\nBatches: 90%|████████▉ | 141/157 [00:28<00:01, 11.44it/s]\u001b[A\nBatches: 91%|█████████ | 143/157 [00:28<00:01, 11.66it/s]\u001b[A\nBatches: 92%|█████████▏| 145/157 [00:29<00:00, 12.00it/s]\u001b[A\nBatches: 94%|█████████▎| 147/157 [00:29<00:00, 12.16it/s]\u001b[A\nBatches: 95%|█████████▍| 149/157 [00:29<00:00, 12.47it/s]\u001b[A\nBatches: 96%|█████████▌| 151/157 [00:29<00:00, 12.74it/s]\u001b[A\nBatches: 97%|█████████▋| 153/157 [00:29<00:00, 13.17it/s]\u001b[A\nBatches: 99%|█████████▊| 155/157 [00:29<00:00, 13.60it/s]\u001b[A\nBatches: 100%|██████████| 157/157 [00:29<00:00, 5.27it/s]\nMap: 34%|███▍ | 5000/14732 [00:30<00:58, 166.33 examples/s]\nBatches: 0%| | 0/157 [00:00<?, ?it/s]\u001b[A\nBatches: 1%| | 1/157 [00:00<01:16, 2.05it/s]\u001b[A\nBatches: 1%|▏ | 2/157 [00:00<01:14, 2.09it/s]\u001b[A\nBatches: 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2.97it/s]\u001b[A\nBatches: 13%|█▎ | 21/157 [00:08<00:45, 2.96it/s]\u001b[A\nBatches: 14%|█▍ | 22/157 [00:08<00:44, 3.04it/s]\u001b[A\nBatches: 15%|█▍ | 23/157 [00:08<00:43, 3.10it/s]\u001b[A\nBatches: 15%|█▌ | 24/157 [00:09<00:42, 3.15it/s]\u001b[A\nBatches: 16%|█▌ | 25/157 [00:09<00:40, 3.22it/s]\u001b[A\nBatches: 17%|█▋ | 26/157 [00:09<00:39, 3.28it/s]\u001b[A\nMap: 34%|███▍ | 5000/14732 [00:40<00:58, 166.33 examples/s]\nBatches: 17%|█▋ | 27/157 [00:10<00:38, 3.38it/s]\u001b[A\nBatches: 18%|█▊ | 28/157 [00:10<00:37, 3.40it/s]\u001b[A\nBatches: 18%|█▊ | 29/157 [00:10<00:36, 3.51it/s]\u001b[A\nBatches: 19%|█▉ | 30/157 [00:10<00:35, 3.60it/s]\u001b[A\nBatches: 20%|█▉ | 31/157 [00:11<00:34, 3.66it/s]\u001b[A\nBatches: 20%|██ | 32/157 [00:11<00:33, 3.70it/s]\u001b[A\nBatches: 21%|██ | 33/157 [00:11<00:32, 3.77it/s]\u001b[A\nBatches: 22%|██▏ | 34/157 [00:11<00:32, 3.81it/s]\u001b[A\nBatches: 22%|██▏ | 35/157 [00:12<00:31, 3.85it/s]\u001b[A\nBatches: 23%|██▎ | 36/157 [00:12<00:30, 3.92it/s]\u001b[A\nBatches: 24%|██▎ | 37/157 [00:12<00:29, 4.01it/s]\u001b[A\nBatches: 24%|██▍ | 38/157 [00:12<00:29, 4.02it/s]\u001b[A\nBatches: 25%|██▍ | 39/157 [00:13<00:29, 4.00it/s]\u001b[A\nBatches: 25%|██▌ | 40/157 [00:13<00:28, 4.09it/s]\u001b[A\nBatches: 26%|██▌ | 41/157 [00:13<00:28, 4.05it/s]\u001b[A\nBatches: 27%|██▋ | 42/157 [00:13<00:28, 4.09it/s]\u001b[A\nBatches: 27%|██▋ | 43/157 [00:14<00:27, 4.15it/s]\u001b[A\nBatches: 28%|██▊ | 44/157 [00:14<00:26, 4.19it/s]\u001b[A\nBatches: 29%|██▊ | 45/157 [00:14<00:26, 4.22it/s]\u001b[A\nBatches: 29%|██▉ | 46/157 [00:14<00:26, 4.27it/s]\u001b[A\nBatches: 30%|██▉ | 47/157 [00:15<00:25, 4.35it/s]\u001b[A\nBatches: 31%|███ | 48/157 [00:15<00:24, 4.41it/s]\u001b[A\nBatches: 31%|███ | 49/157 [00:15<00:24, 4.46it/s]\u001b[A\nBatches: 32%|███▏ | 50/157 [00:15<00:24, 4.46it/s]\u001b[A\nBatches: 32%|███▏ | 51/157 [00:15<00:22, 4.62it/s]\u001b[A\nBatches: 33%|███▎ | 52/157 [00:16<00:22, 4.70it/s]\u001b[A\nBatches: 34%|███▍ | 53/157 [00:16<00:22, 4.65it/s]\u001b[A\nBatches: 34%|███▍ | 54/157 [00:16<00:21, 4.72it/s]\u001b[A\nBatches: 35%|███▌ | 55/157 [00:16<00:21, 4.79it/s]\u001b[A\nBatches: 36%|███▌ | 56/157 [00:16<00:20, 4.98it/s]\u001b[A\nBatches: 36%|███▋ | 57/157 [00:17<00:19, 5.02it/s]\u001b[A\nBatches: 37%|███▋ | 58/157 [00:17<00:19, 5.15it/s]\u001b[A\nBatches: 38%|███▊ | 59/157 [00:17<00:18, 5.22it/s]\u001b[A\nBatches: 38%|███▊ | 60/157 [00:17<00:18, 5.29it/s]\u001b[A\nBatches: 39%|███▉ | 61/157 [00:17<00:18, 5.33it/s]\u001b[A\nBatches: 39%|███▉ | 62/157 [00:18<00:18, 5.18it/s]\u001b[A\nBatches: 40%|████ | 63/157 [00:18<00:17, 5.30it/s]\u001b[A\nBatches: 41%|████ | 64/157 [00:18<00:17, 5.39it/s]\u001b[A\nBatches: 41%|████▏ | 65/157 [00:18<00:16, 5.52it/s]\u001b[A\nBatches: 42%|████▏ | 66/157 [00:18<00:17, 5.31it/s]\u001b[A\nBatches: 43%|████▎ | 67/157 [00:18<00:16, 5.37it/s]\u001b[A\nBatches: 43%|████▎ | 68/157 [00:19<00:16, 5.40it/s]\u001b[A\nBatches: 44%|████▍ | 69/157 [00:19<00:15, 5.52it/s]\u001b[A\nBatches: 45%|████▍ | 70/157 [00:19<00:15, 5.52it/s]\u001b[A\nBatches: 45%|████▌ | 71/157 [00:19<00:15, 5.65it/s]\u001b[A\nBatches: 46%|████▌ | 72/157 [00:19<00:14, 5.84it/s]\u001b[A\nBatches: 46%|████▋ | 73/157 [00:19<00:14, 5.89it/s]\u001b[A\nBatches: 47%|████▋ | 74/157 [00:20<00:14, 5.83it/s]\u001b[A\nBatches: 48%|████▊ | 75/157 [00:20<00:14, 5.83it/s]\u001b[A\nBatches: 48%|████▊ | 76/157 [00:20<00:13, 5.89it/s]\u001b[A\nBatches: 49%|████▉ | 77/157 [00:20<00:13, 6.12it/s]\u001b[A\nBatches: 50%|████▉ | 78/157 [00:20<00:12, 6.20it/s]\u001b[A\nBatches: 50%|█████ | 79/157 [00:20<00:12, 6.23it/s]\u001b[A\nBatches: 51%|█████ | 80/157 [00:21<00:12, 6.29it/s]\u001b[A\nBatches: 52%|█████▏ | 81/157 [00:21<00:12, 6.20it/s]\u001b[A\nBatches: 52%|█████▏ | 82/157 [00:21<00:12, 6.23it/s]\u001b[A\nBatches: 53%|█████▎ | 83/157 [00:21<00:11, 6.35it/s]\u001b[A\nBatches: 54%|█████▎ | 84/157 [00:21<00:11, 6.36it/s]\u001b[A\nBatches: 54%|█████▍ | 85/157 [00:21<00:11, 6.49it/s]\u001b[A\nBatches: 55%|█████▍ | 86/157 [00:22<00:11, 6.29it/s]\u001b[A\nBatches: 55%|█████▌ | 87/157 [00:22<00:10, 6.47it/s]\u001b[A\nBatches: 56%|█████▌ | 88/157 [00:22<00:10, 6.60it/s]\u001b[A\nBatches: 57%|█████▋ | 89/157 [00:22<00:10, 6.71it/s]\u001b[A\nBatches: 57%|█████▋ | 90/157 [00:22<00:09, 6.76it/s]\u001b[A\nBatches: 58%|█████▊ | 91/157 [00:22<00:10, 6.52it/s]\u001b[A\nBatches: 59%|█████▊ | 92/157 [00:22<00:09, 6.66it/s]\u001b[A\nBatches: 59%|█████▉ | 93/157 [00:23<00:09, 6.84it/s]\u001b[A\nBatches: 60%|█████▉ | 94/157 [00:23<00:08, 7.17it/s]\u001b[A\nBatches: 61%|██████ | 95/157 [00:23<00:08, 7.43it/s]\u001b[A\nBatches: 61%|██████ | 96/157 [00:23<00:07, 7.65it/s]\u001b[A\nBatches: 62%|██████▏ | 97/157 [00:23<00:07, 7.52it/s]\u001b[A\nBatches: 62%|██████▏ | 98/157 [00:23<00:08, 7.33it/s]\u001b[A\nBatches: 63%|██████▎ | 99/157 [00:23<00:07, 7.31it/s]\u001b[A\nBatches: 64%|██████▎ | 100/157 [00:24<00:07, 7.55it/s]\u001b[A\nBatches: 64%|██████▍ | 101/157 [00:24<00:07, 7.82it/s]\u001b[A\nBatches: 65%|██████▍ | 102/157 [00:24<00:06, 7.95it/s]\u001b[A\nBatches: 66%|██████▌ | 103/157 [00:24<00:06, 8.02it/s]\u001b[A\nBatches: 66%|██████▌ | 104/157 [00:24<00:06, 8.08it/s]\u001b[A\nBatches: 67%|██████▋ | 105/157 [00:24<00:06, 8.12it/s]\u001b[A\nBatches: 68%|██████▊ | 106/157 [00:24<00:06, 8.22it/s]\u001b[A\nBatches: 68%|██████▊ | 107/157 [00:24<00:05, 8.36it/s]\u001b[A\nBatches: 69%|██████▉ | 108/157 [00:24<00:05, 8.43it/s]\u001b[A\nBatches: 69%|██████▉ | 109/157 [00:25<00:05, 8.44it/s]\u001b[A\nBatches: 70%|███████ | 110/157 [00:25<00:05, 8.49it/s]\u001b[A\nBatches: 71%|███████ | 111/157 [00:25<00:05, 8.58it/s]\u001b[A\nBatches: 71%|███████▏ | 112/157 [00:25<00:05, 8.70it/s]\u001b[A\nBatches: 72%|███████▏ | 113/157 [00:25<00:05, 8.78it/s]\u001b[A\nBatches: 73%|███████▎ | 114/157 [00:25<00:04, 8.87it/s]\u001b[A\nBatches: 73%|███████▎ | 115/157 [00:25<00:04, 8.84it/s]\u001b[A\nBatches: 74%|███████▍ | 116/157 [00:25<00:04, 8.69it/s]\u001b[A\nBatches: 75%|███████▍ | 117/157 [00:25<00:04, 8.89it/s]\u001b[A\nBatches: 75%|███████▌ | 118/157 [00:26<00:04, 8.94it/s]\u001b[A\nBatches: 76%|███████▌ | 119/157 [00:26<00:04, 9.09it/s]\u001b[A\nBatches: 76%|███████▋ | 120/157 [00:26<00:04, 9.18it/s]\u001b[A\nBatches: 78%|███████▊ | 122/157 [00:26<00:03, 9.51it/s]\u001b[A\nBatches: 79%|███████▉ | 124/157 [00:26<00:03, 9.73it/s]\u001b[A\nBatches: 80%|████████ | 126/157 [00:26<00:03, 9.91it/s]\u001b[A\nBatches: 82%|████████▏ | 128/157 [00:27<00:02, 10.33it/s]\u001b[A\nBatches: 83%|████████▎ | 130/157 [00:27<00:02, 10.61it/s]\u001b[A\nBatches: 84%|████████▍ | 132/157 [00:27<00:02, 10.85it/s]\u001b[A\nBatches: 85%|████████▌ | 134/157 [00:27<00:02, 10.89it/s]\u001b[A\nBatches: 87%|████████▋ | 136/157 [00:27<00:01, 11.21it/s]\u001b[A\nBatches: 88%|████████▊ | 138/157 [00:27<00:01, 11.45it/s]\u001b[A\nBatches: 89%|████████▉ | 140/157 [00:28<00:01, 11.61it/s]\u001b[A\nBatches: 90%|█████████ | 142/157 [00:28<00:01, 11.86it/s]\u001b[A\nBatches: 92%|█████████▏| 144/157 [00:28<00:01, 12.13it/s]\u001b[A\nBatches: 93%|█████████▎| 146/157 [00:28<00:00, 12.29it/s]\u001b[A\nBatches: 94%|█████████▍| 148/157 [00:28<00:00, 12.58it/s]\u001b[A\nBatches: 96%|█████████▌| 150/157 [00:28<00:00, 12.87it/s]\u001b[A\nBatches: 97%|█████████▋| 152/157 [00:29<00:00, 13.26it/s]\u001b[A\nBatches: 98%|█████████▊| 154/157 [00:29<00:00, 13.55it/s]\u001b[A\nBatches: 99%|█████████▉| 156/157 [00:29<00:00, 14.14it/s]\u001b[A\nBatches: 100%|██████████| 157/157 [00:29<00:00, 5.36it/s]\nMap: 68%|██████▊ | 10000/14732 [00:59<00:28, 168.03 examples/s]\nBatches: 0%| | 0/148 [00:00<?, ?it/s]\u001b[A\nBatches: 1%| | 1/148 [00:00<01:11, 2.06it/s]\u001b[A\nBatches: 1%|▏ | 2/148 [00:00<01:09, 2.09it/s]\u001b[A\nBatches: 2%|▏ | 3/148 [00:01<01:09, 2.10it/s]\u001b[A\nBatches: 3%|▎ | 4/148 [00:01<01:08, 2.11it/s]\u001b[A\nBatches: 3%|▎ | 5/148 [00:02<01:07, 2.11it/s]\u001b[A\nBatches: 4%|▍ | 6/148 [00:02<01:05, 2.18it/s]\u001b[A\nBatches: 5%|▍ | 7/148 [00:03<01:03, 2.23it/s]\u001b[A\nBatches: 5%|▌ | 8/148 [00:03<01:01, 2.29it/s]\u001b[A\nBatches: 6%|▌ | 9/148 [00:04<00:59, 2.32it/s]\u001b[A\nBatches: 7%|▋ | 10/148 [00:04<00:58, 2.38it/s]\u001b[A\nBatches: 7%|▋ | 11/148 [00:04<00:56, 2.41it/s]\u001b[A\nBatches: 8%|▊ | 12/148 [00:05<00:55, 2.46it/s]\u001b[A\nBatches: 9%|▉ | 13/148 [00:05<00:53, 2.51it/s]\u001b[A\nBatches: 9%|▉ | 14/148 [00:05<00:51, 2.59it/s]\u001b[A\nBatches: 10%|█ | 15/148 [00:06<00:50, 2.66it/s]\u001b[A\nBatches: 11%|█ | 16/148 [00:06<00:48, 2.72it/s]\u001b[A\nBatches: 11%|█▏ | 17/148 [00:07<00:46, 2.82it/s]\u001b[A\nBatches: 12%|█▏ | 18/148 [00:07<00:45, 2.86it/s]\u001b[A\nBatches: 13%|█▎ | 19/148 [00:07<00:44, 2.93it/s]\u001b[A\nBatches: 14%|█▎ | 20/148 [00:07<00:42, 3.03it/s]\u001b[A\nBatches: 14%|█▍ | 21/148 [00:08<00:40, 3.11it/s]\u001b[A\nBatches: 15%|█▍ | 22/148 [00:08<00:40, 3.14it/s]\u001b[A\nBatches: 16%|█▌ | 23/148 [00:08<00:38, 3.21it/s]\u001b[A\nBatches: 16%|█▌ | 24/148 [00:09<00:37, 3.33it/s]\u001b[A\nBatches: 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167.83 examples/s]\nSaved embeddings to /tmp/embeddings.npy. To load, run `np.load('/tmp/embeddings.npy')`",
"metrics": {
"predict_time": 90.556493,
"total_time": 162.814115
},
"output": "https://replicate.delivery/pbxt/6YAHCm6m5ZpoKBzqcT9seN98GRhlFhhqPbzFyuwP8FTRTh1IA/embeddings.npy",
"started_at": "2023-10-05T08:43:53.885240Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/jxrcdk3bqc2j6tbjb4fc5nonmm",
"cancel": "https://api.replicate.com/v1/predictions/jxrcdk3bqc2j6tbjb4fc5nonmm/cancel"
},
"version": "9cf9f015a9cb9c61d1a2610659cdac4a4ca222f2d3707a68517b18c198a9add1"
}Generated in
Torch cuda available True
torch cuda.get_device_name(0) NVIDIA A40
ort.get_available_providers() ['TensorrtExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider']
Generating train split: 0 examples [00:00, ? examples/s]
Generating train split: 0 examples [00:00, ? examples/s]
Failed to load as jsonl, trying as if it's a JSON list of strings
Parameter 'function'=<function Predictor.predict.<locals>.process at 0x7f88e310fca0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
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Batches: 69%|██████▉ | 109/157 [00:25<00:05, 8.48it/s]
Batches: 70%|███████ | 110/157 [00:25<00:05, 8.49it/s]
Batches: 71%|███████ | 111/157 [00:25<00:05, 8.51it/s]
Batches: 71%|███████▏ | 112/157 [00:25<00:05, 8.50it/s]
Batches: 72%|███████▏ | 113/157 [00:25<00:05, 8.54it/s]
Batches: 73%|███████▎ | 114/157 [00:26<00:04, 8.65it/s]
Batches: 73%|███████▎ | 115/157 [00:26<00:04, 8.75it/s]
Batches: 74%|███████▍ | 116/157 [00:26<00:04, 8.74it/s]
Batches: 75%|███████▍ | 117/157 [00:26<00:04, 8.71it/s]
Batches: 75%|███████▌ | 118/157 [00:26<00:04, 8.65it/s]
Batches: 76%|███████▌ | 119/157 [00:26<00:04, 8.72it/s]
Batches: 76%|███████▋ | 120/157 [00:26<00:04, 8.94it/s]
Batches: 77%|███████▋ | 121/157 [00:26<00:03, 9.12it/s]
Batches: 78%|███████▊ | 122/157 [00:26<00:03, 8.96it/s]
Batches: 79%|███████▉ | 124/157 [00:27<00:03, 9.31it/s]
Batches: 80%|███████▉ | 125/157 [00:27<00:03, 9.36it/s]
Batches: 81%|████████ | 127/157 [00:27<00:03, 9.84it/s]
Batches: 82%|████████▏ | 129/157 [00:27<00:02, 10.17it/s]
Batches: 83%|████████▎ | 131/157 [00:27<00:02, 10.58it/s]
Batches: 85%|████████▍ | 133/157 [00:27<00:02, 10.88it/s]
Batches: 86%|████████▌ | 135/157 [00:28<00:01, 11.11it/s]
Batches: 87%|████████▋ | 137/157 [00:28<00:01, 10.98it/s]
Batches: 89%|████████▊ | 139/157 [00:28<00:01, 11.27it/s]
Batches: 90%|████████▉ | 141/157 [00:28<00:01, 11.44it/s]
Batches: 91%|█████████ | 143/157 [00:28<00:01, 11.66it/s]
Batches: 92%|█████████▏| 145/157 [00:29<00:00, 12.00it/s]
Batches: 94%|█████████▎| 147/157 [00:29<00:00, 12.16it/s]
Batches: 95%|█████████▍| 149/157 [00:29<00:00, 12.47it/s]
Batches: 96%|█████████▌| 151/157 [00:29<00:00, 12.74it/s]
Batches: 97%|█████████▋| 153/157 [00:29<00:00, 13.17it/s]
Batches: 99%|█████████▊| 155/157 [00:29<00:00, 13.60it/s]
Batches: 100%|██████████| 157/157 [00:29<00:00, 5.27it/s]
Map: 34%|███▍ | 5000/14732 [00:30<00:58, 166.33 examples/s]
Batches: 0%| | 0/157 [00:00<?, ?it/s]
Batches: 1%| | 1/157 [00:00<01:16, 2.05it/s]
Batches: 1%|▏ | 2/157 [00:00<01:14, 2.09it/s]
Batches: 2%|▏ | 3/157 [00:01<01:13, 2.10it/s]
Batches: 3%|▎ | 4/157 [00:01<01:12, 2.10it/s]
Batches: 3%|▎ | 5/157 [00:02<01:12, 2.11it/s]
Batches: 4%|▍ | 6/157 [00:02<01:10, 2.13it/s]
Batches: 4%|▍ | 7/157 [00:03<01:08, 2.17it/s]
Batches: 5%|▌ | 8/157 [00:03<01:06, 2.24it/s]
Batches: 6%|▌ | 9/157 [00:04<01:04, 2.30it/s]
Batches: 6%|▋ | 10/157 [00:04<01:02, 2.36it/s]
Batches: 7%|▋ | 11/157 [00:04<01:00, 2.43it/s]
Batches: 8%|▊ | 12/157 [00:05<00:57, 2.53it/s]
Batches: 8%|▊ | 13/157 [00:05<00:55, 2.58it/s]
Batches: 9%|▉ | 14/157 [00:05<00:53, 2.66it/s]
Batches: 10%|▉ | 15/157 [00:06<00:51, 2.74it/s]
Batches: 10%|█ | 16/157 [00:06<00:50, 2.79it/s]
Batches: 11%|█ | 17/157 [00:06<00:48, 2.88it/s]
Batches: 11%|█▏ | 18/157 [00:07<00:47, 2.91it/s]
Batches: 12%|█▏ | 19/157 [00:07<00:47, 2.92it/s]
Batches: 13%|█▎ | 20/157 [00:07<00:46, 2.97it/s]
Batches: 13%|█▎ | 21/157 [00:08<00:45, 2.96it/s]
Batches: 14%|█▍ | 22/157 [00:08<00:44, 3.04it/s]
Batches: 15%|█▍ | 23/157 [00:08<00:43, 3.10it/s]
Batches: 15%|█▌ | 24/157 [00:09<00:42, 3.15it/s]
Batches: 16%|█▌ | 25/157 [00:09<00:40, 3.22it/s]
Batches: 17%|█▋ | 26/157 [00:09<00:39, 3.28it/s]
Map: 34%|███▍ | 5000/14732 [00:40<00:58, 166.33 examples/s]
Batches: 17%|█▋ | 27/157 [00:10<00:38, 3.38it/s]
Batches: 18%|█▊ | 28/157 [00:10<00:37, 3.40it/s]
Batches: 18%|█▊ | 29/157 [00:10<00:36, 3.51it/s]
Batches: 19%|█▉ | 30/157 [00:10<00:35, 3.60it/s]
Batches: 20%|█▉ | 31/157 [00:11<00:34, 3.66it/s]
Batches: 20%|██ | 32/157 [00:11<00:33, 3.70it/s]
Batches: 21%|██ | 33/157 [00:11<00:32, 3.77it/s]
Batches: 22%|██▏ | 34/157 [00:11<00:32, 3.81it/s]
Batches: 22%|██▏ | 35/157 [00:12<00:31, 3.85it/s]
Batches: 23%|██▎ | 36/157 [00:12<00:30, 3.92it/s]
Batches: 24%|██▎ | 37/157 [00:12<00:29, 4.01it/s]
Batches: 24%|██▍ | 38/157 [00:12<00:29, 4.02it/s]
Batches: 25%|██▍ | 39/157 [00:13<00:29, 4.00it/s]
Batches: 25%|██▌ | 40/157 [00:13<00:28, 4.09it/s]
Batches: 26%|██▌ | 41/157 [00:13<00:28, 4.05it/s]
Batches: 27%|██▋ | 42/157 [00:13<00:28, 4.09it/s]
Batches: 27%|██▋ | 43/157 [00:14<00:27, 4.15it/s]
Batches: 28%|██▊ | 44/157 [00:14<00:26, 4.19it/s]
Batches: 29%|██▊ | 45/157 [00:14<00:26, 4.22it/s]
Batches: 29%|██▉ | 46/157 [00:14<00:26, 4.27it/s]
Batches: 30%|██▉ | 47/157 [00:15<00:25, 4.35it/s]
Batches: 31%|███ | 48/157 [00:15<00:24, 4.41it/s]
Batches: 31%|███ | 49/157 [00:15<00:24, 4.46it/s]
Batches: 32%|███▏ | 50/157 [00:15<00:24, 4.46it/s]
Batches: 32%|███▏ | 51/157 [00:15<00:22, 4.62it/s]
Batches: 33%|███▎ | 52/157 [00:16<00:22, 4.70it/s]
Batches: 34%|███▍ | 53/157 [00:16<00:22, 4.65it/s]
Batches: 34%|███▍ | 54/157 [00:16<00:21, 4.72it/s]
Batches: 35%|███▌ | 55/157 [00:16<00:21, 4.79it/s]
Batches: 36%|███▌ | 56/157 [00:16<00:20, 4.98it/s]
Batches: 36%|███▋ | 57/157 [00:17<00:19, 5.02it/s]
Batches: 37%|███▋ | 58/157 [00:17<00:19, 5.15it/s]
Batches: 38%|███▊ | 59/157 [00:17<00:18, 5.22it/s]
Batches: 38%|███▊ | 60/157 [00:17<00:18, 5.29it/s]
Batches: 39%|███▉ | 61/157 [00:17<00:18, 5.33it/s]
Batches: 39%|███▉ | 62/157 [00:18<00:18, 5.18it/s]
Batches: 40%|████ | 63/157 [00:18<00:17, 5.30it/s]
Batches: 41%|████ | 64/157 [00:18<00:17, 5.39it/s]
Batches: 41%|████▏ | 65/157 [00:18<00:16, 5.52it/s]
Batches: 42%|████▏ | 66/157 [00:18<00:17, 5.31it/s]
Batches: 43%|████▎ | 67/157 [00:18<00:16, 5.37it/s]
Batches: 43%|████▎ | 68/157 [00:19<00:16, 5.40it/s]
Batches: 44%|████▍ | 69/157 [00:19<00:15, 5.52it/s]
Batches: 45%|████▍ | 70/157 [00:19<00:15, 5.52it/s]
Batches: 45%|████▌ | 71/157 [00:19<00:15, 5.65it/s]
Batches: 46%|████▌ | 72/157 [00:19<00:14, 5.84it/s]
Batches: 46%|████▋ | 73/157 [00:19<00:14, 5.89it/s]
Batches: 47%|████▋ | 74/157 [00:20<00:14, 5.83it/s]
Batches: 48%|████▊ | 75/157 [00:20<00:14, 5.83it/s]
Batches: 48%|████▊ | 76/157 [00:20<00:13, 5.89it/s]
Batches: 49%|████▉ | 77/157 [00:20<00:13, 6.12it/s]
Batches: 50%|████▉ | 78/157 [00:20<00:12, 6.20it/s]
Batches: 50%|█████ | 79/157 [00:20<00:12, 6.23it/s]
Batches: 51%|█████ | 80/157 [00:21<00:12, 6.29it/s]
Batches: 52%|█████▏ | 81/157 [00:21<00:12, 6.20it/s]
Batches: 52%|█████▏ | 82/157 [00:21<00:12, 6.23it/s]
Batches: 53%|█████▎ | 83/157 [00:21<00:11, 6.35it/s]
Batches: 54%|█████▎ | 84/157 [00:21<00:11, 6.36it/s]
Batches: 54%|█████▍ | 85/157 [00:21<00:11, 6.49it/s]
Batches: 55%|█████▍ | 86/157 [00:22<00:11, 6.29it/s]
Batches: 55%|█████▌ | 87/157 [00:22<00:10, 6.47it/s]
Batches: 56%|█████▌ | 88/157 [00:22<00:10, 6.60it/s]
Batches: 57%|█████▋ | 89/157 [00:22<00:10, 6.71it/s]
Batches: 57%|█████▋ | 90/157 [00:22<00:09, 6.76it/s]
Batches: 58%|█████▊ | 91/157 [00:22<00:10, 6.52it/s]
Batches: 59%|█████▊ | 92/157 [00:22<00:09, 6.66it/s]
Batches: 59%|█████▉ | 93/157 [00:23<00:09, 6.84it/s]
Batches: 60%|█████▉ | 94/157 [00:23<00:08, 7.17it/s]
Batches: 61%|██████ | 95/157 [00:23<00:08, 7.43it/s]
Batches: 61%|██████ | 96/157 [00:23<00:07, 7.65it/s]
Batches: 62%|██████▏ | 97/157 [00:23<00:07, 7.52it/s]
Batches: 62%|██████▏ | 98/157 [00:23<00:08, 7.33it/s]
Batches: 63%|██████▎ | 99/157 [00:23<00:07, 7.31it/s]
Batches: 64%|██████▎ | 100/157 [00:24<00:07, 7.55it/s]
Batches: 64%|██████▍ | 101/157 [00:24<00:07, 7.82it/s]
Batches: 65%|██████▍ | 102/157 [00:24<00:06, 7.95it/s]
Batches: 66%|██████▌ | 103/157 [00:24<00:06, 8.02it/s]
Batches: 66%|██████▌ | 104/157 [00:24<00:06, 8.08it/s]
Batches: 67%|██████▋ | 105/157 [00:24<00:06, 8.12it/s]
Batches: 68%|██████▊ | 106/157 [00:24<00:06, 8.22it/s]
Batches: 68%|██████▊ | 107/157 [00:24<00:05, 8.36it/s]
Batches: 69%|██████▉ | 108/157 [00:24<00:05, 8.43it/s]
Batches: 69%|██████▉ | 109/157 [00:25<00:05, 8.44it/s]
Batches: 70%|███████ | 110/157 [00:25<00:05, 8.49it/s]
Batches: 71%|███████ | 111/157 [00:25<00:05, 8.58it/s]
Batches: 71%|███████▏ | 112/157 [00:25<00:05, 8.70it/s]
Batches: 72%|███████▏ | 113/157 [00:25<00:05, 8.78it/s]
Batches: 73%|███████▎ | 114/157 [00:25<00:04, 8.87it/s]
Batches: 73%|███████▎ | 115/157 [00:25<00:04, 8.84it/s]
Batches: 74%|███████▍ | 116/157 [00:25<00:04, 8.69it/s]
Batches: 75%|███████▍ | 117/157 [00:25<00:04, 8.89it/s]
Batches: 75%|███████▌ | 118/157 [00:26<00:04, 8.94it/s]
Batches: 76%|███████▌ | 119/157 [00:26<00:04, 9.09it/s]
Batches: 76%|███████▋ | 120/157 [00:26<00:04, 9.18it/s]
Batches: 78%|███████▊ | 122/157 [00:26<00:03, 9.51it/s]
Batches: 79%|███████▉ | 124/157 [00:26<00:03, 9.73it/s]
Batches: 80%|████████ | 126/157 [00:26<00:03, 9.91it/s]
Batches: 82%|████████▏ | 128/157 [00:27<00:02, 10.33it/s]
Batches: 83%|████████▎ | 130/157 [00:27<00:02, 10.61it/s]
Batches: 84%|████████▍ | 132/157 [00:27<00:02, 10.85it/s]
Batches: 85%|████████▌ | 134/157 [00:27<00:02, 10.89it/s]
Batches: 87%|████████▋ | 136/157 [00:27<00:01, 11.21it/s]
Batches: 88%|████████▊ | 138/157 [00:27<00:01, 11.45it/s]
Batches: 89%|████████▉ | 140/157 [00:28<00:01, 11.61it/s]
Batches: 90%|█████████ | 142/157 [00:28<00:01, 11.86it/s]
Batches: 92%|█████████▏| 144/157 [00:28<00:01, 12.13it/s]
Batches: 93%|█████████▎| 146/157 [00:28<00:00, 12.29it/s]
Batches: 94%|█████████▍| 148/157 [00:28<00:00, 12.58it/s]
Batches: 96%|█████████▌| 150/157 [00:28<00:00, 12.87it/s]
Batches: 97%|█████████▋| 152/157 [00:29<00:00, 13.26it/s]
Batches: 98%|█████████▊| 154/157 [00:29<00:00, 13.55it/s]
Batches: 99%|█████████▉| 156/157 [00:29<00:00, 14.14it/s]
Batches: 100%|██████████| 157/157 [00:29<00:00, 5.36it/s]
Map: 68%|██████▊ | 10000/14732 [00:59<00:28, 168.03 examples/s]
Batches: 0%| | 0/148 [00:00<?, ?it/s]
Batches: 1%| | 1/148 [00:00<01:11, 2.06it/s]
Batches: 1%|▏ | 2/148 [00:00<01:09, 2.09it/s]
Batches: 2%|▏ | 3/148 [00:01<01:09, 2.10it/s]
Batches: 3%|▎ | 4/148 [00:01<01:08, 2.11it/s]
Batches: 3%|▎ | 5/148 [00:02<01:07, 2.11it/s]
Batches: 4%|▍ | 6/148 [00:02<01:05, 2.18it/s]
Batches: 5%|▍ | 7/148 [00:03<01:03, 2.23it/s]
Batches: 5%|▌ | 8/148 [00:03<01:01, 2.29it/s]
Batches: 6%|▌ | 9/148 [00:04<00:59, 2.32it/s]
Batches: 7%|▋ | 10/148 [00:04<00:58, 2.38it/s]
Batches: 7%|▋ | 11/148 [00:04<00:56, 2.41it/s]
Batches: 8%|▊ | 12/148 [00:05<00:55, 2.46it/s]
Batches: 9%|▉ | 13/148 [00:05<00:53, 2.51it/s]
Batches: 9%|▉ | 14/148 [00:05<00:51, 2.59it/s]
Batches: 10%|█ | 15/148 [00:06<00:50, 2.66it/s]
Batches: 11%|█ | 16/148 [00:06<00:48, 2.72it/s]
Batches: 11%|█▏ | 17/148 [00:07<00:46, 2.82it/s]
Batches: 12%|█▏ | 18/148 [00:07<00:45, 2.86it/s]
Batches: 13%|█▎ | 19/148 [00:07<00:44, 2.93it/s]
Batches: 14%|█▎ | 20/148 [00:07<00:42, 3.03it/s]
Batches: 14%|█▍ | 21/148 [00:08<00:40, 3.11it/s]
Batches: 15%|█▍ | 22/148 [00:08<00:40, 3.14it/s]
Batches: 16%|█▌ | 23/148 [00:08<00:38, 3.21it/s]
Batches: 16%|█▌ | 24/148 [00:09<00:37, 3.33it/s]
Batches: 17%|█▋ | 25/148 [00:09<00:37, 3.30it/s]
Batches: 18%|█▊ | 26/148 [00:09<00:36, 3.35it/s]
Map: 68%|██████▊ | 10000/14732 [01:09<00:28, 168.03 examples/s]
Batches: 18%|█▊ | 27/148 [00:10<00:35, 3.44it/s]
Batches: 19%|█▉ | 28/148 [00:10<00:33, 3.54it/s]
Batches: 20%|█▉ | 29/148 [00:10<00:33, 3.59it/s]
Batches: 20%|██ | 30/148 [00:10<00:32, 3.64it/s]
Batches: 21%|██ | 31/148 [00:11<00:31, 3.68it/s]
Batches: 22%|██▏ | 32/148 [00:11<00:30, 3.76it/s]
Batches: 22%|██▏ | 33/148 [00:11<00:30, 3.79it/s]
Batches: 23%|██▎ | 34/148 [00:11<00:29, 3.80it/s]
Batches: 24%|██▎ | 35/148 [00:12<00:29, 3.88it/s]
Batches: 24%|██▍ | 36/148 [00:12<00:28, 3.91it/s]
Batches: 25%|██▌ | 37/148 [00:12<00:28, 3.86it/s]
Batches: 26%|██▌ | 38/148 [00:12<00:27, 3.95it/s]
Batches: 26%|██▋ | 39/148 [00:13<00:27, 4.03it/s]
Batches: 27%|██▋ | 40/148 [00:13<00:26, 4.10it/s]
Batches: 28%|██▊ | 41/148 [00:13<00:25, 4.18it/s]
Batches: 28%|██▊ | 42/148 [00:13<00:25, 4.24it/s]
Batches: 29%|██▉ | 43/148 [00:14<00:24, 4.25it/s]
Batches: 30%|██▉ | 44/148 [00:14<00:24, 4.20it/s]
Batches: 30%|███ | 45/148 [00:14<00:24, 4.29it/s]
Batches: 31%|███ | 46/148 [00:14<00:23, 4.32it/s]
Batches: 32%|███▏ | 47/148 [00:14<00:23, 4.31it/s]
Batches: 32%|███▏ | 48/148 [00:15<00:22, 4.37it/s]
Batches: 33%|███▎ | 49/148 [00:15<00:22, 4.44it/s]
Batches: 34%|███▍ | 50/148 [00:15<00:20, 4.67it/s]
Batches: 34%|███▍ | 51/148 [00:15<00:20, 4.66it/s]
Batches: 35%|███▌ | 52/148 [00:15<00:19, 4.82it/s]
Batches: 36%|███▌ | 53/148 [00:16<00:20, 4.73it/s]
Batches: 36%|███▋ | 54/148 [00:16<00:19, 4.79it/s]
Batches: 37%|███▋ | 55/148 [00:16<00:18, 4.99it/s]
Batches: 38%|███▊ | 56/148 [00:16<00:18, 5.07it/s]
Batches: 39%|███▊ | 57/148 [00:16<00:17, 5.07it/s]
Batches: 39%|███▉ | 58/148 [00:17<00:17, 5.19it/s]
Batches: 40%|███▉ | 59/148 [00:17<00:16, 5.30it/s]
Batches: 41%|████ | 60/148 [00:17<00:16, 5.38it/s]
Batches: 41%|████ | 61/148 [00:17<00:16, 5.41it/s]
Batches: 42%|████▏ | 62/148 [00:17<00:15, 5.50it/s]
Batches: 43%|████▎ | 63/148 [00:18<00:15, 5.54it/s]
Batches: 43%|████▎ | 64/148 [00:18<00:14, 5.62it/s]
Batches: 44%|████▍ | 65/148 [00:18<00:14, 5.68it/s]
Batches: 45%|████▍ | 66/148 [00:18<00:14, 5.69it/s]
Batches: 45%|████▌ | 67/148 [00:18<00:14, 5.64it/s]
Batches: 46%|████▌ | 68/148 [00:18<00:14, 5.71it/s]
Batches: 47%|████▋ | 69/148 [00:19<00:13, 5.74it/s]
Batches: 47%|████▋ | 70/148 [00:19<00:13, 5.80it/s]
Batches: 48%|████▊ | 71/148 [00:19<00:12, 5.93it/s]
Batches: 49%|████▊ | 72/148 [00:19<00:12, 6.06it/s]
Batches: 49%|████▉ | 73/148 [00:19<00:12, 6.05it/s]
Batches: 50%|█████ | 74/148 [00:19<00:11, 6.18it/s]
Batches: 51%|█████ | 75/148 [00:20<00:11, 6.26it/s]
Batches: 51%|█████▏ | 76/148 [00:20<00:11, 6.34it/s]
Batches: 52%|█████▏ | 77/148 [00:20<00:11, 6.39it/s]
Batches: 53%|█████▎ | 78/148 [00:20<00:10, 6.47it/s]
Batches: 53%|█████▎ | 79/148 [00:20<00:10, 6.54it/s]
Batches: 54%|█████▍ | 80/148 [00:20<00:10, 6.52it/s]
Batches: 55%|█████▍ | 81/148 [00:20<00:10, 6.61it/s]
Batches: 55%|█████▌ | 82/148 [00:21<00:09, 6.70it/s]
Batches: 56%|█████▌ | 83/148 [00:21<00:09, 6.75it/s]
Batches: 57%|█████▋ | 84/148 [00:21<00:09, 6.83it/s]
Batches: 57%|█████▋ | 85/148 [00:21<00:09, 6.83it/s]
Batches: 58%|█████▊ | 86/148 [00:21<00:09, 6.87it/s]
Batches: 59%|█████▉ | 87/148 [00:21<00:08, 6.93it/s]
Batches: 59%|█████▉ | 88/148 [00:21<00:08, 6.92it/s]
Batches: 60%|██████ | 89/148 [00:22<00:08, 6.98it/s]
Batches: 61%|██████ | 90/148 [00:22<00:08, 7.03it/s]
Batches: 61%|██████▏ | 91/148 [00:22<00:08, 7.06it/s]
Batches: 62%|██████▏ | 92/148 [00:22<00:07, 7.35it/s]
Batches: 63%|██████▎ | 93/148 [00:22<00:07, 7.57it/s]
Batches: 64%|██████▎ | 94/148 [00:22<00:07, 7.47it/s]
Batches: 64%|██████▍ | 95/148 [00:22<00:07, 7.39it/s]
Batches: 65%|██████▍ | 96/148 [00:23<00:06, 7.65it/s]
Batches: 66%|██████▌ | 97/148 [00:23<00:06, 7.84it/s]
Batches: 66%|██████▌ | 98/148 [00:23<00:06, 8.06it/s]
Batches: 67%|██████▋ | 99/148 [00:23<00:06, 8.16it/s]
Batches: 68%|██████▊ | 100/148 [00:23<00:05, 8.15it/s]
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Batches: 71%|███████ | 105/148 [00:24<00:05, 8.55it/s]
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Batches: 76%|███████▌ | 112/148 [00:24<00:03, 9.10it/s]
Batches: 76%|███████▋ | 113/148 [00:24<00:03, 9.08it/s]
Batches: 77%|███████▋ | 114/148 [00:25<00:03, 9.13it/s]
Batches: 78%|███████▊ | 115/148 [00:25<00:03, 9.24it/s]
Batches: 78%|███████▊ | 116/148 [00:25<00:03, 9.31it/s]
Batches: 79%|███████▉ | 117/148 [00:25<00:03, 9.39it/s]
Batches: 80%|████████ | 119/148 [00:25<00:02, 9.76it/s]
Batches: 82%|████████▏ | 121/148 [00:25<00:02, 9.93it/s]
Batches: 83%|████████▎ | 123/148 [00:25<00:02, 10.21it/s]
Batches: 84%|████████▍ | 125/148 [00:26<00:02, 10.40it/s]
Batches: 86%|████████▌ | 127/148 [00:26<00:01, 10.76it/s]
Batches: 87%|████████▋ | 129/148 [00:26<00:01, 10.93it/s]
Batches: 89%|████████▊ | 131/148 [00:26<00:01, 11.33it/s]
Batches: 90%|████████▉ | 133/148 [00:26<00:01, 11.56it/s]
Batches: 91%|█████████ | 135/148 [00:26<00:01, 11.86it/s]
Batches: 93%|█████████▎| 137/148 [00:27<00:00, 12.09it/s]
Batches: 94%|█████████▍| 139/148 [00:27<00:00, 12.36it/s]
Batches: 95%|█████████▌| 141/148 [00:27<00:00, 12.64it/s]
Batches: 97%|█████████▋| 143/148 [00:27<00:00, 12.75it/s]
Batches: 98%|█████████▊| 145/148 [00:27<00:00, 13.21it/s]
Batches: 99%|█████████▉| 147/148 [00:27<00:00, 13.92it/s]
Batches: 100%|██████████| 148/148 [00:27<00:00, 5.30it/s]
Map: 100%|██████████| 14732/14732 [01:27<00:00, 168.01 examples/s]
Map: 100%|██████████| 14732/14732 [01:27<00:00, 167.83 examples/s]
Saved embeddings to /tmp/embeddings.npy. To load, run `np.load('/tmp/embeddings.npy')`