Specify library_name metadata
#1
by
tomaarsen
HF Staff
- opened
README.md
CHANGED
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@@ -946,7 +946,7 @@ model-index:
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| 946 |
- type: precision_at_10
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| 947 |
value: 5.743
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| 948 |
- type: precision_at_100
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| 949 |
-
value: 1
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| 950 |
- type: precision_at_1000
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| 951 |
value: 0.14300000000000002
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| 952 |
- type: precision_at_3
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@@ -2223,7 +2223,7 @@ model-index:
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| 2223 |
- type: map_at_5
|
| 2224 |
value: 70.15
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| 2225 |
- type: mrr_at_1
|
| 2226 |
-
value: 64
|
| 2227 |
- type: mrr_at_10
|
| 2228 |
value: 71.82300000000001
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| 2229 |
- type: mrr_at_100
|
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@@ -2235,7 +2235,7 @@ model-index:
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| 2235 |
- type: mrr_at_5
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| 2236 |
value: 71.11699999999999
|
| 2237 |
- type: ndcg_at_1
|
| 2238 |
-
value: 64
|
| 2239 |
- type: ndcg_at_10
|
| 2240 |
value: 75.286
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| 2241 |
- type: ndcg_at_100
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@@ -2247,7 +2247,7 @@ model-index:
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| 2247 |
- type: ndcg_at_5
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| 2248 |
value: 73.36399999999999
|
| 2249 |
- type: precision_at_1
|
| 2250 |
-
value: 64
|
| 2251 |
- type: precision_at_10
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| 2252 |
value: 9.9
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| 2253 |
- type: precision_at_100
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@@ -2288,7 +2288,7 @@ model-index:
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|
| 2288 |
- type: cos_sim_precision
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| 2289 |
value: 92.3076923076923
|
| 2290 |
- type: cos_sim_recall
|
| 2291 |
-
value: 90
|
| 2292 |
- type: dot_accuracy
|
| 2293 |
value: 99.7980198019802
|
| 2294 |
- type: dot_ap
|
|
@@ -2399,7 +2399,7 @@ model-index:
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|
| 2399 |
- type: map_at_5
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| 2400 |
value: 0.941
|
| 2401 |
- type: mrr_at_1
|
| 2402 |
-
value: 76
|
| 2403 |
- type: mrr_at_10
|
| 2404 |
value: 85.85199999999999
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| 2405 |
- type: mrr_at_100
|
|
@@ -2411,7 +2411,7 @@ model-index:
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|
| 2411 |
- type: mrr_at_5
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| 2412 |
value: 85.56700000000001
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| 2413 |
- type: ndcg_at_1
|
| 2414 |
-
value: 71
|
| 2415 |
- type: ndcg_at_10
|
| 2416 |
value: 69.60300000000001
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| 2417 |
- type: ndcg_at_100
|
|
@@ -2423,7 +2423,7 @@ model-index:
|
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| 2423 |
- type: ndcg_at_5
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| 2424 |
value: 71.17599999999999
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| 2425 |
- type: precision_at_1
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| 2426 |
-
value: 76
|
| 2427 |
- type: precision_at_10
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| 2428 |
value: 74.2
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| 2429 |
- type: precision_at_100
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|
@@ -2456,13 +2456,13 @@ model-index:
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| 2456 |
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
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| 2457 |
metrics:
|
| 2458 |
- type: accuracy
|
| 2459 |
-
value: 8
|
| 2460 |
- type: f1
|
| 2461 |
value: 6.298401229470593
|
| 2462 |
- type: precision
|
| 2463 |
value: 5.916991709050532
|
| 2464 |
- type: recall
|
| 2465 |
-
value: 8
|
| 2466 |
- task:
|
| 2467 |
type: BitextMining
|
| 2468 |
dataset:
|
|
@@ -2592,13 +2592,13 @@ model-index:
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|
| 2592 |
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
| 2593 |
metrics:
|
| 2594 |
- type: accuracy
|
| 2595 |
-
value: 22
|
| 2596 |
- type: f1
|
| 2597 |
value: 17.4576947358322
|
| 2598 |
- type: precision
|
| 2599 |
value: 16.261363669827777
|
| 2600 |
- type: recall
|
| 2601 |
-
value: 22
|
| 2602 |
- task:
|
| 2603 |
type: BitextMining
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| 2604 |
dataset:
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@@ -2745,13 +2745,13 @@ model-index:
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|
| 2745 |
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
| 2746 |
metrics:
|
| 2747 |
- type: accuracy
|
| 2748 |
-
value: 0
|
| 2749 |
- type: f1
|
| 2750 |
-
value: 0
|
| 2751 |
- type: precision
|
| 2752 |
-
value: 0
|
| 2753 |
- type: recall
|
| 2754 |
-
value: 0
|
| 2755 |
- task:
|
| 2756 |
type: BitextMining
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| 2757 |
dataset:
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@@ -2813,13 +2813,13 @@ model-index:
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|
| 2813 |
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
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| 2814 |
metrics:
|
| 2815 |
- type: accuracy
|
| 2816 |
-
value: 4
|
| 2817 |
- type: f1
|
| 2818 |
value: 2.3800704501963432
|
| 2819 |
- type: precision
|
| 2820 |
value: 2.0919368034607455
|
| 2821 |
- type: recall
|
| 2822 |
-
value: 4
|
| 2823 |
- task:
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| 2824 |
type: BitextMining
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| 2825 |
dataset:
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@@ -2949,13 +2949,13 @@ model-index:
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| 2949 |
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
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| 2950 |
metrics:
|
| 2951 |
- type: accuracy
|
| 2952 |
-
value: 21
|
| 2953 |
- type: f1
|
| 2954 |
value: 18.965901242066018
|
| 2955 |
- type: precision
|
| 2956 |
value: 18.381437375171
|
| 2957 |
- type: recall
|
| 2958 |
-
value: 21
|
| 2959 |
- task:
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| 2960 |
type: BitextMining
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| 2961 |
dataset:
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@@ -3119,13 +3119,13 @@ model-index:
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| 3119 |
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
| 3120 |
metrics:
|
| 3121 |
- type: accuracy
|
| 3122 |
-
value: 1
|
| 3123 |
- type: f1
|
| 3124 |
value: 0.7764001197963462
|
| 3125 |
- type: precision
|
| 3126 |
value: 0.7551049317943337
|
| 3127 |
- type: recall
|
| 3128 |
-
value: 1
|
| 3129 |
- task:
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| 3130 |
type: BitextMining
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| 3131 |
dataset:
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@@ -3289,13 +3289,13 @@ model-index:
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| 3289 |
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
| 3290 |
metrics:
|
| 3291 |
- type: accuracy
|
| 3292 |
-
value: 6
|
| 3293 |
- type: f1
|
| 3294 |
value: 4.58716840215435
|
| 3295 |
- type: precision
|
| 3296 |
value: 4.303119297298687
|
| 3297 |
- type: recall
|
| 3298 |
-
value: 6
|
| 3299 |
- task:
|
| 3300 |
type: BitextMining
|
| 3301 |
dataset:
|
|
@@ -3833,13 +3833,13 @@ model-index:
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|
| 3833 |
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
| 3834 |
metrics:
|
| 3835 |
- type: accuracy
|
| 3836 |
-
value: 10
|
| 3837 |
- type: f1
|
| 3838 |
value: 7.351901305737391
|
| 3839 |
- type: precision
|
| 3840 |
value: 6.759061952118555
|
| 3841 |
- type: recall
|
| 3842 |
-
value: 10
|
| 3843 |
- task:
|
| 3844 |
type: BitextMining
|
| 3845 |
dataset:
|
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@@ -3918,13 +3918,13 @@ model-index:
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|
| 3918 |
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
| 3919 |
metrics:
|
| 3920 |
- type: accuracy
|
| 3921 |
-
value: 3
|
| 3922 |
- type: f1
|
| 3923 |
value: 1.5834901411814404
|
| 3924 |
- type: precision
|
| 3925 |
value: 1.3894010894944848
|
| 3926 |
- type: recall
|
| 3927 |
-
value: 3
|
| 3928 |
- task:
|
| 3929 |
type: BitextMining
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| 3930 |
dataset:
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@@ -4571,6 +4571,7 @@ model-index:
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| 4571 |
language:
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| 4572 |
- en
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| 4573 |
license: mit
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| 4574 |
---
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| 4575 |
<h1 align="center">GIST Embedding v0</h1>
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| 4576 |
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@@ -4648,4 +4649,4 @@ The model was evaluated using the [MTEB Evaluation](https://huggingface.co/mteb)
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| 4648 |
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| 4649 |
This work is supported by the "KCP IV - Exploring Data Use in the Development Economics Literature using Large Language Models (AI and LLMs)" project funded by the [Knowledge for Change Program (KCP)](https://www.worldbank.org/en/programs/knowledge-for-change) of the World Bank - RA-P503405-RESE-TF0C3444.
|
| 4650 |
|
| 4651 |
-
The findings, interpretations, and conclusions expressed in this material are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
|
|
|
|
| 946 |
- type: precision_at_10
|
| 947 |
value: 5.743
|
| 948 |
- type: precision_at_100
|
| 949 |
+
value: 1
|
| 950 |
- type: precision_at_1000
|
| 951 |
value: 0.14300000000000002
|
| 952 |
- type: precision_at_3
|
|
|
|
| 2223 |
- type: map_at_5
|
| 2224 |
value: 70.15
|
| 2225 |
- type: mrr_at_1
|
| 2226 |
+
value: 64
|
| 2227 |
- type: mrr_at_10
|
| 2228 |
value: 71.82300000000001
|
| 2229 |
- type: mrr_at_100
|
|
|
|
| 2235 |
- type: mrr_at_5
|
| 2236 |
value: 71.11699999999999
|
| 2237 |
- type: ndcg_at_1
|
| 2238 |
+
value: 64
|
| 2239 |
- type: ndcg_at_10
|
| 2240 |
value: 75.286
|
| 2241 |
- type: ndcg_at_100
|
|
|
|
| 2247 |
- type: ndcg_at_5
|
| 2248 |
value: 73.36399999999999
|
| 2249 |
- type: precision_at_1
|
| 2250 |
+
value: 64
|
| 2251 |
- type: precision_at_10
|
| 2252 |
value: 9.9
|
| 2253 |
- type: precision_at_100
|
|
|
|
| 2288 |
- type: cos_sim_precision
|
| 2289 |
value: 92.3076923076923
|
| 2290 |
- type: cos_sim_recall
|
| 2291 |
+
value: 90
|
| 2292 |
- type: dot_accuracy
|
| 2293 |
value: 99.7980198019802
|
| 2294 |
- type: dot_ap
|
|
|
|
| 2399 |
- type: map_at_5
|
| 2400 |
value: 0.941
|
| 2401 |
- type: mrr_at_1
|
| 2402 |
+
value: 76
|
| 2403 |
- type: mrr_at_10
|
| 2404 |
value: 85.85199999999999
|
| 2405 |
- type: mrr_at_100
|
|
|
|
| 2411 |
- type: mrr_at_5
|
| 2412 |
value: 85.56700000000001
|
| 2413 |
- type: ndcg_at_1
|
| 2414 |
+
value: 71
|
| 2415 |
- type: ndcg_at_10
|
| 2416 |
value: 69.60300000000001
|
| 2417 |
- type: ndcg_at_100
|
|
|
|
| 2423 |
- type: ndcg_at_5
|
| 2424 |
value: 71.17599999999999
|
| 2425 |
- type: precision_at_1
|
| 2426 |
+
value: 76
|
| 2427 |
- type: precision_at_10
|
| 2428 |
value: 74.2
|
| 2429 |
- type: precision_at_100
|
|
|
|
| 2456 |
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
| 2457 |
metrics:
|
| 2458 |
- type: accuracy
|
| 2459 |
+
value: 8
|
| 2460 |
- type: f1
|
| 2461 |
value: 6.298401229470593
|
| 2462 |
- type: precision
|
| 2463 |
value: 5.916991709050532
|
| 2464 |
- type: recall
|
| 2465 |
+
value: 8
|
| 2466 |
- task:
|
| 2467 |
type: BitextMining
|
| 2468 |
dataset:
|
|
|
|
| 2592 |
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
| 2593 |
metrics:
|
| 2594 |
- type: accuracy
|
| 2595 |
+
value: 22
|
| 2596 |
- type: f1
|
| 2597 |
value: 17.4576947358322
|
| 2598 |
- type: precision
|
| 2599 |
value: 16.261363669827777
|
| 2600 |
- type: recall
|
| 2601 |
+
value: 22
|
| 2602 |
- task:
|
| 2603 |
type: BitextMining
|
| 2604 |
dataset:
|
|
|
|
| 2745 |
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
| 2746 |
metrics:
|
| 2747 |
- type: accuracy
|
| 2748 |
+
value: 0
|
| 2749 |
- type: f1
|
| 2750 |
+
value: 0
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| 2751 |
- type: precision
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| 2752 |
+
value: 0
|
| 2753 |
- type: recall
|
| 2754 |
+
value: 0
|
| 2755 |
- task:
|
| 2756 |
type: BitextMining
|
| 2757 |
dataset:
|
|
|
|
| 2813 |
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
| 2814 |
metrics:
|
| 2815 |
- type: accuracy
|
| 2816 |
+
value: 4
|
| 2817 |
- type: f1
|
| 2818 |
value: 2.3800704501963432
|
| 2819 |
- type: precision
|
| 2820 |
value: 2.0919368034607455
|
| 2821 |
- type: recall
|
| 2822 |
+
value: 4
|
| 2823 |
- task:
|
| 2824 |
type: BitextMining
|
| 2825 |
dataset:
|
|
|
|
| 2949 |
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
| 2950 |
metrics:
|
| 2951 |
- type: accuracy
|
| 2952 |
+
value: 21
|
| 2953 |
- type: f1
|
| 2954 |
value: 18.965901242066018
|
| 2955 |
- type: precision
|
| 2956 |
value: 18.381437375171
|
| 2957 |
- type: recall
|
| 2958 |
+
value: 21
|
| 2959 |
- task:
|
| 2960 |
type: BitextMining
|
| 2961 |
dataset:
|
|
|
|
| 3119 |
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
| 3120 |
metrics:
|
| 3121 |
- type: accuracy
|
| 3122 |
+
value: 1
|
| 3123 |
- type: f1
|
| 3124 |
value: 0.7764001197963462
|
| 3125 |
- type: precision
|
| 3126 |
value: 0.7551049317943337
|
| 3127 |
- type: recall
|
| 3128 |
+
value: 1
|
| 3129 |
- task:
|
| 3130 |
type: BitextMining
|
| 3131 |
dataset:
|
|
|
|
| 3289 |
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
| 3290 |
metrics:
|
| 3291 |
- type: accuracy
|
| 3292 |
+
value: 6
|
| 3293 |
- type: f1
|
| 3294 |
value: 4.58716840215435
|
| 3295 |
- type: precision
|
| 3296 |
value: 4.303119297298687
|
| 3297 |
- type: recall
|
| 3298 |
+
value: 6
|
| 3299 |
- task:
|
| 3300 |
type: BitextMining
|
| 3301 |
dataset:
|
|
|
|
| 3833 |
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
| 3834 |
metrics:
|
| 3835 |
- type: accuracy
|
| 3836 |
+
value: 10
|
| 3837 |
- type: f1
|
| 3838 |
value: 7.351901305737391
|
| 3839 |
- type: precision
|
| 3840 |
value: 6.759061952118555
|
| 3841 |
- type: recall
|
| 3842 |
+
value: 10
|
| 3843 |
- task:
|
| 3844 |
type: BitextMining
|
| 3845 |
dataset:
|
|
|
|
| 3918 |
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
| 3919 |
metrics:
|
| 3920 |
- type: accuracy
|
| 3921 |
+
value: 3
|
| 3922 |
- type: f1
|
| 3923 |
value: 1.5834901411814404
|
| 3924 |
- type: precision
|
| 3925 |
value: 1.3894010894944848
|
| 3926 |
- type: recall
|
| 3927 |
+
value: 3
|
| 3928 |
- task:
|
| 3929 |
type: BitextMining
|
| 3930 |
dataset:
|
|
|
|
| 4571 |
language:
|
| 4572 |
- en
|
| 4573 |
license: mit
|
| 4574 |
+
library_name: sentence-transformers
|
| 4575 |
---
|
| 4576 |
<h1 align="center">GIST Embedding v0</h1>
|
| 4577 |
|
|
|
|
| 4649 |
|
| 4650 |
This work is supported by the "KCP IV - Exploring Data Use in the Development Economics Literature using Large Language Models (AI and LLMs)" project funded by the [Knowledge for Change Program (KCP)](https://www.worldbank.org/en/programs/knowledge-for-change) of the World Bank - RA-P503405-RESE-TF0C3444.
|
| 4651 |
|
| 4652 |
+
The findings, interpretations, and conclusions expressed in this material are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
|