nateraw / bge-large-en-v1.5

BAAI's bge-en-large-v1.5 for embedding text sequences

Cold

Public
294.1K runs
L40S
GitHub
Paper

Run with an API

Playground API Examples README Versions

Prediction

nateraw/bge-large-en-v1.5:9cf9f015a9cb9c61d1a2610659cdac4a4ca222f2d3707a68517b18c198a9add1

Model

nateraw/bge-large-en-v1.5:9cf9f015

jxrcdk3bqc2j6tbjb4fc5nonmm

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

2m 43s

Created

over 1 year ago by @nateraw

Input

path: samsum_as_list.txt
texts
batch_size: 32
convert_to_numpy
normalize_embeddings

{
  "path": "https://replicate.delivery/pbxt/Je5WZ8zvLRFwGKANUdGLuc5xu599R2JF5I4RvxLpuTUg7ek5/samsum_as_list.txt",
  "texts": "",
  "batch_size": 32,
  "convert_to_numpy": true,
  "normalize_embeddings": true
}

Install Replicate’s Node.js client library:

npm install replicate

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 replicate

Import the client:

import replicate

Run 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.

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/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

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, 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2.10it/s]\u001b[A\nBatches:   3%|▎         | 5/157 [00:02<01:12,  2.11it/s]\u001b[A\nBatches:   4%|▍         | 6/157 [00:02<01:10,  2.13it/s]\u001b[A\nBatches:   4%|▍         | 7/157 [00:03<01:08,  2.17it/s]\u001b[A\nBatches:   5%|▌         | 8/157 [00:03<01:06,  2.24it/s]\u001b[A\nBatches:   6%|▌         | 9/157 [00:04<01:04,  2.30it/s]\u001b[A\nBatches:   6%|▋         | 10/157 [00:04<01:02,  2.36it/s]\u001b[A\nBatches:   7%|▋         | 11/157 [00:04<01:00,  2.43it/s]\u001b[A\nBatches:   8%|▊         | 12/157 [00:05<00:57,  2.53it/s]\u001b[A\nBatches:   8%|▊         | 13/157 [00:05<00:55,  2.58it/s]\u001b[A\nBatches:   9%|▉         | 14/157 [00:05<00:53,  2.66it/s]\u001b[A\nBatches:  10%|▉         | 15/157 [00:06<00:51,  2.74it/s]\u001b[A\nBatches:  10%|█         | 16/157 [00:06<00:50,  2.79it/s]\u001b[A\nBatches:  11%|█         | 17/157 [00:06<00:48,  2.88it/s]\u001b[A\nBatches:  11%|█▏        | 18/157 [00:07<00:47,  2.91it/s]\u001b[A\nBatches:  12%|█▏        | 19/157 [00:07<00:47,  2.92it/s]\u001b[A\nBatches:  13%|█▎        | 20/157 [00:07<00:46,  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,  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12.09it/s]\u001b[A\nBatches:  94%|█████████▍| 139/148 [00:27<00:00, 12.36it/s]\u001b[A\nBatches:  95%|█████████▌| 141/148 [00:27<00:00, 12.64it/s]\u001b[A\nBatches:  97%|█████████▋| 143/148 [00:27<00:00, 12.75it/s]\u001b[A\nBatches:  98%|█████████▊| 145/148 [00:27<00:00, 13.21it/s]\u001b[A\nBatches:  99%|█████████▉| 147/148 [00:27<00:00, 13.92it/s]\u001b[A\nBatches: 100%|██████████| 148/148 [00:27<00:00,  5.30it/s]\nMap: 100%|██████████| 14732/14732 [01:27<00:00, 168.01 examples/s]\nMap: 100%|██████████| 14732/14732 [01:27<00:00, 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

1 minute 31 seconds

Tweak it Share Report

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:  48%|████▊     | 75/157 [00:20<00:13,  5.90it/s]
Batches:  48%|████▊     | 76/157 [00:20<00:13,  5.92it/s]
Batches:  49%|████▉     | 77/157 [00:21<00:13,  5.92it/s]
Batches:  50%|████▉     | 78/157 [00:21<00:13,  5.87it/s]
Batches:  50%|█████     | 79/157 [00:21<00:13,  6.00it/s]
Batches:  51%|█████     | 80/157 [00:21<00:12,  5.99it/s]
Batches:  52%|█████▏    | 81/157 [00:21<00:12,  6.08it/s]
Batches:  52%|█████▏    | 82/157 [00:21<00:12,  6.23it/s]
Batches:  53%|█████▎    | 83/157 [00:22<00:11,  6.27it/s]
Batches:  54%|█████▎    | 84/157 [00:22<00:11,  6.40it/s]
Batches:  54%|█████▍    | 85/157 [00:22<00:11,  6.40it/s]
Batches:  55%|█████▍    | 86/157 [00:22<00:10,  6.52it/s]
Batches:  55%|█████▌    | 87/157 [00:22<00:10,  6.52it/s]
Batches:  56%|█████▌    | 88/157 [00:22<00:10,  6.47it/s]
Batches:  57%|█████▋    | 89/157 [00:22<00:10,  6.59it/s]
Batches:  57%|█████▋    | 90/157 [00:23<00:10,  6.68it/s]
Batches:  58%|█████▊    | 91/157 [00:23<00:09,  6.77it/s]
Batches:  59%|█████▊    | 92/157 [00:23<00:09,  6.86it/s]
Batches:  59%|█████▉    | 93/157 [00:23<00:09,  6.97it/s]
Batches:  60%|█████▉    | 94/157 [00:23<00:08,  7.07it/s]
Batches:  61%|██████    | 95/157 [00:23<00:08,  7.10it/s]
Batches:  61%|██████    | 96/157 [00:23<00:08,  7.37it/s]
Batches:  62%|██████▏   | 97/157 [00:24<00:08,  7.29it/s]
Batches:  62%|██████▏   | 98/157 [00:24<00:08,  7.27it/s]
Batches:  63%|██████▎   | 99/157 [00:24<00:07,  7.53it/s]
Batches:  64%|██████▎   | 100/157 [00:24<00:07,  7.72it/s]
Batches:  64%|██████▍   | 101/157 [00:24<00:07,  7.78it/s]
Batches:  65%|██████▍   | 102/157 [00:24<00:06,  7.88it/s]
Batches:  66%|██████▌   | 103/157 [00:24<00:06,  7.95it/s]
Batches:  66%|██████▌   | 104/157 [00:24<00:06,  8.06it/s]
Batches:  67%|██████▋   | 105/157 [00:25<00:06,  8.22it/s]
Batches:  68%|██████▊   | 106/157 [00:25<00:06,  8.32it/s]
Batches:  68%|██████▊   | 107/157 [00:25<00:05,  8.42it/s]
Batches:  69%|██████▉   | 108/157 [00:25<00:05,  8.46it/s]
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]
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Map: 100%|██████████| 14732/14732 [01:27<00:00, 168.01 examples/s]
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Saved embeddings to /tmp/embeddings.npy. To load, run `np.load('/tmp/embeddings.npy')`

Prediction

nateraw/bge-large-en-v1.5:9cf9f015a9cb9c61d1a2610659cdac4a4ca222f2d3707a68517b18c198a9add1

Model

nateraw/bge-large-en-v1.5:9cf9f015

wnn37y3boxihxnv2kyrk4atkiu

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

81.3s

Created

over 1 year ago

Input

path: null
texts: ["In the water, fish are swimming.", "Fish swim in the water.", "A book lies open on the table."]
batch_size: 32
convert_to_numpy
normalize_embeddings

{
  "path": null,
  "texts": "[\"In the water, fish are swimming.\", \"Fish swim in the water.\", \"A book lies open on the table.\"]",
  "batch_size": 32,
  "convert_to_numpy": true,
  "normalize_embeddings": true
}

Install Replicate’s Node.js client library:

npm install replicate

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: {
      texts: "[\"In the water, fish are swimming.\", \"Fish swim in the water.\", \"A book lies open on the table.\"]",
      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 replicate

Import the client:

import replicate

Run 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={
        "texts": "[\"In the water, fish are swimming.\", \"Fish swim in the water.\", \"A book lies open on the table.\"]",
        "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.

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": {
      "texts": "[\\"In the water, fish are swimming.\\", \\"Fish swim in the water.\\", \\"A book lies open on the table.\\"]",
      "batch_size": 32,
      "convert_to_numpy": true,
      "normalize_embeddings": true
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

https://replicate.delivery/pbxt/bkf7f94q2qp2OE9cP7XbFy3Tee3x01i1SKveJ1syjonv5VYNC/embeddings.npy

{
  "completed_at": "2023-10-05T08:54:37.825893Z",
  "created_at": "2023-10-05T08:53:16.508248Z",
  "data_removed": false,
  "error": null,
  "id": "wnn37y3boxihxnv2kyrk4atkiu",
  "input": {
    "path": null,
    "texts": "[\"In the water, fish are swimming.\", \"Fish swim in the water.\", \"A book lies open on the table.\"]",
    "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']\nParameter 'function'=<function Predictor.predict.<locals>.process at 0x7fc2940b2820> 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%|          | 0/3 [00:00<?, ? examples/s]\nBatches:   0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\nBatches: 100%|██████████| 1/1 [00:00<00:00, 86.99it/s]\nMap: 100%|██████████| 3/3 [00:00<00:00, 201.72 examples/s]\nSaved embeddings to /tmp/embeddings.npy. To load, run `np.load('/tmp/embeddings.npy')`",
  "metrics": {
    "predict_time": 1.023847,
    "total_time": 81.317645
  },
  "output": "https://replicate.delivery/pbxt/bkf7f94q2qp2OE9cP7XbFy3Tee3x01i1SKveJ1syjonv5VYNC/embeddings.npy",
  "started_at": "2023-10-05T08:54:36.802046Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/wnn37y3boxihxnv2kyrk4atkiu",
    "cancel": "https://api.replicate.com/v1/predictions/wnn37y3boxihxnv2kyrk4atkiu/cancel"
  },
  "version": "9cf9f015a9cb9c61d1a2610659cdac4a4ca222f2d3707a68517b18c198a9add1"
}

Generated in

1.0 second

Tweak it Share Report

Prediction

nateraw/bge-large-en-v1.5:9cf9f015a9cb9c61d1a2610659cdac4a4ca222f2d3707a68517b18c198a9add1

Model

nateraw/bge-large-en-v1.5:9cf9f015

2f5irttb5rbv6cw4pyvkczn4p4

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

-1959ms

Created

over 1 year ago

Input

path: null
texts: ["In the water, fish are swimming.", "Fish swim in the water.", "A book lies open on the table."]
batch_size: 32
convert_to_numpy
normalize_embeddings

{
  "path": null,
  "texts": "[\"In the water, fish are swimming.\", \"Fish swim in the water.\", \"A book lies open on the table.\"]",
  "batch_size": 32,
  "convert_to_numpy": false,
  "normalize_embeddings": true
}

Install Replicate’s Node.js client library:

npm install replicate

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: {
      texts: "[\"In the water, fish are swimming.\", \"Fish swim in the water.\", \"A book lies open on the table.\"]",
      batch_size: 32,
      convert_to_numpy: false,
      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 replicate

Import the client:

import replicate

Run 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={
        "texts": "[\"In the water, fish are swimming.\", \"Fish swim in the water.\", \"A book lies open on the table.\"]",
        "batch_size": 32,
        "convert_to_numpy": False,
        "normalize_embeddings": True
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

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": {
      "texts": "[\\"In the water, fish are swimming.\\", \\"Fish swim in the water.\\", \\"A book lies open on the table.\\"]",
      "batch_size": 32,
      "convert_to_numpy": false,
      "normalize_embeddings": true
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

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{
  "completed_at": "2023-10-05T08:56:05.853799Z",
  "created_at": "2023-10-05T08:56:07.812466Z",
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  "error": null,
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    "texts": "[\"In the water, fish are swimming.\", \"Fish swim in the water.\", \"A book lies open on the table.\"]",
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0.3 seconds

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