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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:4984 |
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- loss:MultipleNegativesRankingLoss |
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base_model: sentence-transformers/all-MiniLM-L6-v2 |
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widget: |
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- source_sentence: '<think> |
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Let’s think through this step by step |
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rp = 500/month |
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fp = 10/day |
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lp = 60/lesson |
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lpw = 2 lessons/week |
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yp = 12 months/year |
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total = (500 × 12) + (10 × 365) + (60 × 2 × 52) |
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total = 6000 + 3650 + 6240 |
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total = 15890 |
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</think> |
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\boxed{15890}' |
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sentences: |
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- '<think> |
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Let’s think through this step by step |
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rp = 500/month |
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fp = 10/day |
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lp = 60/lesson |
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tp = (500 × 12) + (10 × 365) + (60 × 2 × 52) |
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tp = 6000 + 3650 + 6240 |
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tp = 15890 |
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</think> |
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\boxed{15890}' |
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- '<think> |
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Let’s think through this step by step |
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h1 = 500 ft |
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h2 = 2 * h1 = 2 * 500 = 1000 ft |
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Total height = h1 + h2 = 500 + 1000 = 1500 ft |
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</think> |
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\boxed{1500}' |
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- '<think> |
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Let’s think through this step by step |
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fc = 200 |
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lc = 500 |
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dc = 500 × 0.8 = 400 |
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nc = 200 - 50 = 150 |
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tc = 150 + 400 = 550 |
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</think> |
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\boxed{550}' |
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- source_sentence: '<think> |
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Let’s think through this step by step |
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s = 5 MB/song |
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m1 = 10 songs |
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m2 = 15 songs |
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m3 = 3 songs |
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total = (10 + 15 + 3) × 5 |
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total = 28 × 5 = 140 MB |
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</think> |
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\boxed{140}' |
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sentences: |
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- '<think> |
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Let’s think through this step by step |
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s = 5 MB/song |
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m1 = 10 songs |
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m2 = 15 songs |
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m3 = 3 songs |
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Total memory = (10 + 15 + 3) × 5 |
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Total memory = 28 × 5 = 140 MB |
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</think> |
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\boxed{140}' |
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- '<think> |
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Let’s think through this step by step |
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total_slices = 12 |
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eaten_slices = 12 × (1/4) = 3 |
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kept_slices = 12 - 3 = 9 |
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</think> |
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\boxed{9}' |
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- '<think> |
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Let’s think through this step by step |
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Bald Mountain = 1.5 meters = 150 cm |
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Billy Mountain = 3.5 meters = 350 cm |
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Mount Pilot = 126 cm |
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Difference between Billy Mountain and Bald Mountain = 350 - 150 = 200 cm |
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Difference between Mount Pilot and Bald Mountain = 126 - 150 = -24 cm |
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</think> |
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\boxed{200}' |
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- source_sentence: '<think> |
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Let’s think through this step by step |
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Strength training: 1 hour/session × 3 sessions/week = 3 hours/week |
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Boxing training: 1.5 hours/session × 4 sessions/week = 6 hours/week |
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Total training hours per week = 3 + 6 = 9 hours/week |
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</think> |
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\boxed{9}' |
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sentences: |
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- '<think> |
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Let’s think through this step by step |
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t = 5 |
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p = 4 |
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s = 6 |
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r = 5 / 2 = 2.5 |
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income = (2 × 5) + 4 + (4 × 6) + (6 × 2.5) |
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income = 10 + 4 + 24 + 15 |
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income = 53 |
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</think> |
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\boxed{53}' |
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- '<think> |
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Let’s think through this step by step |
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Strength training: 1 hour/session × 3 sessions/week = 3 hours/week |
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Boxing training: 1.5 hours/session × 4 sessions/week = 6 hours/week |
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Total training hours per week = 3 + 6 = 9 hours/week |
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</think> |
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\boxed{9}' |
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- '<think> |
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Let’s think through this step by step |
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Let d be the number of ducks sold |
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Earnings from selling chickens = 5 × 8 = 40 |
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Earnings from selling ducks = 10d |
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Total earnings = 40 + 10d |
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Amount spent on wheelbarrow = (1/2)(40 + 10d) |
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Amount earned from selling wheelbarrow = 60 |
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40 + 10d = 2(60) |
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10d = 80 |
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d = 8 |
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</think> |
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\boxed{8}' |
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- source_sentence: '<think> |
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Let’s think through this step by step |
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up = 3 flights |
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down = 3 flights |
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total = (3 + 3) × 5 = 30 flights |
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</think> |
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\boxed{30}' |
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sentences: |
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- '<think> |
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Let’s think through this step by step |
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up = 3 flights |
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down = 3 flights |
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total = (3 + 3) × 5 = 30 flights |
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</think> |
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\boxed{30}' |
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- '<think> |
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Let’s think through this step by step |
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J = M + 10 |
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J + 10 = 25 |
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(M + 10) + 10 = 25 |
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M + 20 = 25 |
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M = 5 |
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J = 5 + 10 = 15 |
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Sum = 5 + 15 = 20 |
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</think> |
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\boxed{20}' |
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- '<think> |
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Let’s think through this step by step |
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total_money = 7(1) + 4(5) + 2(10) + 1(20) = 7 + 20 + 20 + 20 = 67 |
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cost = 67 - 4 = 63 |
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pounds = 63 / 3 = 21 |
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days = 7 |
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average = 21 / 7 = 3 |
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</think> |
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\boxed{3}' |
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- source_sentence: '<think> |
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Let’s think through this step by step |
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n = 4 |
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sp = 75 |
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t = 36 |
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spt = 36 / 4 = 9 |
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op = 75 + 9 = 84 |
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</think> |
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\boxed{84}' |
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sentences: |
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- '<think> |
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Let’s think through this step by step |
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Let B be Benedict''s house size |
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K = 10000 sq ft |
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K = 4B + 600 |
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10000 = 4B + 600 |
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4B = 9400 |
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B = 2350 sq ft |
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</think> |
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\boxed{2350}' |
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- '<think> |
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Let’s think through this step by step |
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Total throws = 80 |
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No pass thrown = 30% of 80 = 0.3 × 80 = 24 |
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Sacked for a loss = 0.5 × 24 = 12 |
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</think> |
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\boxed{12}' |
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- '<think> |
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Let’s think through this step by step |
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n = 4 |
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sp = 75 |
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t = 36 |
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spt = t / n = 36 / 4 = 9 |
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op = sp + spt = 75 + 9 = 84 |
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</think> |
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\boxed{84}' |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- pearson_cosine |
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- spearman_cosine |
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model-index: |
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- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 |
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results: |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: sts dev |
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type: sts-dev |
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metrics: |
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- type: pearson_cosine |
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value: 0.8793284892973376 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.876484495899188 |
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name: Spearman Cosine |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: sts test |
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type: sts-test |
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metrics: |
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- type: pearson_cosine |
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value: 0.879334132854901 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8764936381058213 |
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name: Spearman Cosine |
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--- |
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# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the csv dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf --> |
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- **Maximum Sequence Length:** 256 tokens |
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- **Output Dimensionality:** 384 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- csv |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("training") |
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# Run inference |
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sentences = [ |
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'<think>\nLet’s think through this step by step\nn = 4\nsp = 75\nt = 36\nspt = 36 / 4 = 9\nop = 75 + 9 = 84\n</think>\n\\boxed{84}', |
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'<think>\nLet’s think through this step by step\nn = 4\nsp = 75\nt = 36\nspt = t / n = 36 / 4 = 9\nop = sp + spt = 75 + 9 = 84\n</think>\n\\boxed{84}', |
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"<think>\nLet’s think through this step by step\nLet B be Benedict's house size\nK = 10000 sq ft\nK = 4B + 600\n10000 = 4B + 600\n4B = 9400\nB = 2350 sq ft\n</think>\n\\boxed{2350}", |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 384] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Semantic Similarity |
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* Datasets: `sts-dev` and `sts-test` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
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| Metric | sts-dev | sts-test | |
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|:--------------------|:-----------|:-----------| |
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| pearson_cosine | 0.8793 | 0.8793 | |
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| **spearman_cosine** | **0.8765** | **0.8765** | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### csv |
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* Dataset: csv |
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* Size: 4,984 training samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | |
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|:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 34 tokens</li><li>mean: 66.41 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 36 tokens</li><li>mean: 67.03 tokens</li><li>max: 256 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | |
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|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code><think><br>Let’s think through this step by step<br>ht_hare = 14 inches<br>ht_camel = 24 × 14 = 336 inches<br>ht_camel_ft = 336 / 12 = 28 feet<br></think><br>\boxed{28}</code> | <code><think><br>Let’s think through this step by step<br>ht_hare = 14 inches<br>ht_camel = 24 * 14 = 336 inches<br>ht_camel_ft = 336 / 12 = 28 feet<br></think><br>\boxed{28}</code> | |
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| <code><think><br>Let’s think through this step by step<br>cpb = 5<br>sp = 90<br>pmpb = 2<br>tp = 5 × 5 = 25<br>tpmp = 5 × 2 = 10<br>profit = 90 - 25 - 10 = 55<br></think><br>\boxed{55}</code> | <code><think><br>Let’s think through this step by step<br>cp = 5 × 5 = 25<br>sp = 90<br>p = sp - cp - (5 × 2) = 90 - 25 - 10 = 55<br></think><br>\boxed{55}</code> | |
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| <code><think><br>Let’s think through this step by step<br>Time to iron a blouse = 15 minutes<br>Time to iron a dress = 20 minutes<br>Time spent on blouses = 2 hours = 120 minutes<br>Time spent on dresses = 3 hours = 180 minutes<br>Number of blouses ironed = 120 / 15 = 8<br>Number of dresses ironed = 180 / 20 = 9<br>Total pieces of clothes ironed = 8 + 9 = 17<br></think><br>\boxed{17}</code> | <code><think><br>Let’s think through this step by step<br>Time to iron 1 blouse = 15 minutes<br>Time to iron 1 dress = 20 minutes<br>Time spent on blouses = 2 hours = 120 minutes<br>Time spent on dresses = 3 hours = 180 minutes<br>Number of blouses ironed = 120 / 15 = 8<br>Number of dresses ironed = 180 / 20 = 9<br>Total pieces of clothes ironed = 8 + 9 = 17<br></think><br>\boxed{17}</code> | |
|
|
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"scale": 20.0, |
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"similarity_fct": "cos_sim" |
|
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} |
|
|
``` |
|
|
|
|
|
### Evaluation Dataset |
|
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|
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#### csv |
|
|
|
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* Dataset: csv |
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* Size: 4,984 evaluation samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
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* Approximate statistics based on the first 1000 samples: |
|
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| | anchor | positive | |
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|:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 33 tokens</li><li>mean: 66.68 tokens</li><li>max: 164 tokens</li></ul> | <ul><li>min: 33 tokens</li><li>mean: 66.71 tokens</li><li>max: 161 tokens</li></ul> | |
|
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* Samples: |
|
|
| anchor | positive | |
|
|
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
|
| <code><think><br>Let’s think through this step by step<br>sg = 36<br>sc = 1/4<br>fl = 1/3<br>sg_left = sg - (sg × sc) - (sg × (1 - sc) × fl)<br>sg_left = 36 - (36 × 1/4) - (36 × (1 - 1/4) × 1/3)<br>sg_left = 36 - 9 - 6<br>sg_left = 21<br></think><br>\boxed{21}</code> | <code><think><br>Let’s think through this step by step<br>sg = 36<br>sc = 1/4<br>fl = 1/3<br>sg_left = sg - (sg × sc) - (sg × (1 - sc) × fl)<br>sg_left = 36 - (36 × 1/4) - (36 × (1 - 1/4) × 1/3)<br>sg_left = 36 - 9 - 6<br>sg_left = 21<br></think><br>\boxed{21}</code> | |
|
|
| <code><think><br>Let’s think through this step by step<br>d1 = 125 miles<br>d2 = 223 miles<br>d3 = 493 - (125 + 223)<br>d3 = 145 miles<br></think><br>\boxed{145}</code> | <code><think><br>Let’s think through this step by step<br>d1 = 125 miles<br>d2 = 223 miles<br>d3 = 493 - (125 + 223)<br>d3 = 145 miles<br></think><br>\boxed{145}</code> | |
|
|
| <code><think><br>Let’s think through this step by step<br>Total workdays = 2 weeks × 5 days/week = 10 days<br>Paid vacation days = 6 days<br>Unpaid vacation days = 10 - 6 = 4 days<br>Total pay = 15 × 8 = $120/day<br>Missed pay = 4 × 120 = $480<br></think><br>\boxed{480}</code> | <code><think><br>Let’s think through this step by step<br>Total workdays = 2 weeks × 5 days/week = 10 days<br>Paid vacation days = 6 days<br>Unpaid vacation days = 10 - 6 = 4 days<br>Total pay = 15 × 8 = $120/day<br>Missed pay = 4 × 120 = $480<br></think><br>\boxed{480}</code> | |
|
|
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
|
|
```json |
|
|
{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
|
|
} |
|
|
``` |
|
|
|
|
|
### Training Hyperparameters |
|
|
#### Non-Default Hyperparameters |
|
|
|
|
|
- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 20 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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- `batch_sampler`: no_duplicates |
|
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
|
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|
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|
- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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|
- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 20 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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|
- `log_level_replica`: warning |
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|
- `log_on_each_node`: True |
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|
- `logging_nan_inf_filter`: True |
|
|
- `save_safetensors`: True |
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|
- `save_on_each_node`: False |
|
|
- `save_only_model`: False |
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|
- `restore_callback_states_from_checkpoint`: False |
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|
- `no_cuda`: False |
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|
- `use_cpu`: False |
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|
- `use_mps_device`: False |
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|
- `seed`: 42 |
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|
- `data_seed`: None |
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|
- `jit_mode_eval`: False |
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|
- `use_ipex`: False |
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|
- `bf16`: False |
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|
- `fp16`: True |
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|
- `fp16_opt_level`: O1 |
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|
- `half_precision_backend`: auto |
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|
- `bf16_full_eval`: False |
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|
- `fp16_full_eval`: False |
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|
- `tf32`: None |
|
|
- `local_rank`: 0 |
|
|
- `ddp_backend`: None |
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|
- `tpu_num_cores`: None |
|
|
- `tpu_metrics_debug`: False |
|
|
- `debug`: [] |
|
|
- `dataloader_drop_last`: False |
|
|
- `dataloader_num_workers`: 0 |
|
|
- `dataloader_prefetch_factor`: None |
|
|
- `past_index`: -1 |
|
|
- `disable_tqdm`: False |
|
|
- `remove_unused_columns`: True |
|
|
- `label_names`: None |
|
|
- `load_best_model_at_end`: False |
|
|
- `ignore_data_skip`: False |
|
|
- `fsdp`: [] |
|
|
- `fsdp_min_num_params`: 0 |
|
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
|
- `tp_size`: 0 |
|
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
|
- `deepspeed`: None |
|
|
- `label_smoothing_factor`: 0.0 |
|
|
- `optim`: adamw_torch |
|
|
- `optim_args`: None |
|
|
- `adafactor`: False |
|
|
- `group_by_length`: False |
|
|
- `length_column_name`: length |
|
|
- `ddp_find_unused_parameters`: None |
|
|
- `ddp_bucket_cap_mb`: None |
|
|
- `ddp_broadcast_buffers`: False |
|
|
- `dataloader_pin_memory`: True |
|
|
- `dataloader_persistent_workers`: False |
|
|
- `skip_memory_metrics`: True |
|
|
- `use_legacy_prediction_loop`: False |
|
|
- `push_to_hub`: False |
|
|
- `resume_from_checkpoint`: None |
|
|
- `hub_model_id`: None |
|
|
- `hub_strategy`: every_save |
|
|
- `hub_private_repo`: None |
|
|
- `hub_always_push`: False |
|
|
- `gradient_checkpointing`: False |
|
|
- `gradient_checkpointing_kwargs`: None |
|
|
- `include_inputs_for_metrics`: False |
|
|
- `include_for_metrics`: [] |
|
|
- `eval_do_concat_batches`: True |
|
|
- `fp16_backend`: auto |
|
|
- `push_to_hub_model_id`: None |
|
|
- `push_to_hub_organization`: None |
|
|
- `mp_parameters`: |
|
|
- `auto_find_batch_size`: False |
|
|
- `full_determinism`: False |
|
|
- `torchdynamo`: None |
|
|
- `ray_scope`: last |
|
|
- `ddp_timeout`: 1800 |
|
|
- `torch_compile`: False |
|
|
- `torch_compile_backend`: None |
|
|
- `torch_compile_mode`: None |
|
|
- `include_tokens_per_second`: False |
|
|
- `include_num_input_tokens_seen`: False |
|
|
- `neftune_noise_alpha`: None |
|
|
- `optim_target_modules`: None |
|
|
- `batch_eval_metrics`: False |
|
|
- `eval_on_start`: False |
|
|
- `use_liger_kernel`: False |
|
|
- `eval_use_gather_object`: False |
|
|
- `average_tokens_across_devices`: False |
|
|
- `prompts`: None |
|
|
- `batch_sampler`: no_duplicates |
|
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
|
|
</details> |
|
|
|
|
|
### Training Logs |
|
|
| Epoch | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |
|
|
|:-------:|:----:|:-------------:|:---------------:|:-----------------------:|:------------------------:| |
|
|
| -1 | -1 | - | - | 0.8671 | - | |
|
|
| 0.3953 | 100 | 0.0422 | 0.0031 | 0.8701 | - | |
|
|
| 0.7905 | 200 | 0.0105 | 0.0017 | 0.8727 | - | |
|
|
| 1.1858 | 300 | 0.0041 | 0.0016 | 0.8728 | - | |
|
|
| 1.5810 | 400 | 0.0016 | 0.0011 | 0.8730 | - | |
|
|
| 1.9763 | 500 | 0.0039 | 0.0021 | 0.8731 | - | |
|
|
| 2.3715 | 600 | 0.0014 | 0.0020 | 0.8741 | - | |
|
|
| 2.7668 | 700 | 0.0014 | 0.0017 | 0.8744 | - | |
|
|
| 3.1621 | 800 | 0.0019 | 0.0009 | 0.8742 | - | |
|
|
| 3.5573 | 900 | 0.0012 | 0.0011 | 0.8754 | - | |
|
|
| 3.9526 | 1000 | 0.0016 | 0.0015 | 0.8760 | - | |
|
|
| 4.3478 | 1100 | 0.0021 | 0.0011 | 0.8763 | - | |
|
|
| 4.7431 | 1200 | 0.0006 | 0.0009 | 0.8753 | - | |
|
|
| 5.1383 | 1300 | 0.0004 | 0.0009 | 0.8753 | - | |
|
|
| 5.5336 | 1400 | 0.0008 | 0.0008 | 0.8751 | - | |
|
|
| 5.9289 | 1500 | 0.0004 | 0.0004 | 0.8743 | - | |
|
|
| 6.3241 | 1600 | 0.0009 | 0.0008 | 0.8758 | - | |
|
|
| 6.7194 | 1700 | 0.0005 | 0.0009 | 0.8747 | - | |
|
|
| 7.1146 | 1800 | 0.0004 | 0.0006 | 0.8742 | - | |
|
|
| 7.5099 | 1900 | 0.0003 | 0.0010 | 0.8748 | - | |
|
|
| 7.9051 | 2000 | 0.0006 | 0.0008 | 0.8742 | - | |
|
|
| 8.3004 | 2100 | 0.0005 | 0.0007 | 0.8744 | - | |
|
|
| 8.6957 | 2200 | 0.0003 | 0.0006 | 0.8748 | - | |
|
|
| 9.0909 | 2300 | 0.0005 | 0.0012 | 0.8749 | - | |
|
|
| 9.4862 | 2400 | 0.0007 | 0.0006 | 0.8762 | - | |
|
|
| 9.8814 | 2500 | 0.0003 | 0.0009 | 0.8762 | - | |
|
|
| 10.2767 | 2600 | 0.0004 | 0.0007 | 0.8759 | - | |
|
|
| 10.6719 | 2700 | 0.0005 | 0.0005 | 0.8760 | - | |
|
|
| 11.0672 | 2800 | 0.0005 | 0.0007 | 0.8754 | - | |
|
|
| 11.4625 | 2900 | 0.0002 | 0.0008 | 0.8749 | - | |
|
|
| 11.8577 | 3000 | 0.0002 | 0.0007 | 0.8749 | - | |
|
|
| 12.2530 | 3100 | 0.0003 | 0.0007 | 0.8752 | - | |
|
|
| 12.6482 | 3200 | 0.0004 | 0.0008 | 0.8760 | - | |
|
|
| 13.0435 | 3300 | 0.0002 | 0.0008 | 0.8767 | - | |
|
|
| 13.4387 | 3400 | 0.0002 | 0.0007 | 0.8763 | - | |
|
|
| 13.8340 | 3500 | 0.0002 | 0.0007 | 0.8763 | - | |
|
|
| 14.2292 | 3600 | 0.0001 | 0.0007 | 0.8764 | - | |
|
|
| 14.6245 | 3700 | 0.0003 | 0.0006 | 0.8765 | - | |
|
|
| 15.0198 | 3800 | 0.0002 | 0.0005 | 0.8757 | - | |
|
|
| 15.4150 | 3900 | 0.0002 | 0.0004 | 0.8760 | - | |
|
|
| 15.8103 | 4000 | 0.0002 | 0.0005 | 0.8765 | - | |
|
|
| 16.2055 | 4100 | 0.0002 | 0.0005 | 0.8757 | - | |
|
|
| 16.6008 | 4200 | 0.0002 | 0.0006 | 0.8758 | - | |
|
|
| 16.9960 | 4300 | 0.0002 | 0.0006 | 0.8758 | - | |
|
|
| 17.3913 | 4400 | 0.0001 | 0.0005 | 0.8761 | - | |
|
|
| 17.7866 | 4500 | 0.0002 | 0.0005 | 0.8765 | - | |
|
|
| 18.1818 | 4600 | 0.0001 | 0.0005 | 0.8767 | - | |
|
|
| 18.5771 | 4700 | 0.0004 | 0.0004 | 0.8765 | - | |
|
|
| 18.9723 | 4800 | 0.0002 | 0.0004 | 0.8765 | - | |
|
|
| 19.3676 | 4900 | 0.0001 | 0.0004 | 0.8765 | - | |
|
|
| 19.7628 | 5000 | 0.0001 | 0.0004 | 0.8765 | - | |
|
|
| -1 | -1 | - | - | - | 0.8765 | |
|
|
|
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.11.11 |
|
|
- Sentence Transformers: 3.4.1 |
|
|
- Transformers: 4.51.1 |
|
|
- PyTorch: 2.5.1+cu124 |
|
|
- Accelerate: 1.3.0 |
|
|
- Datasets: 3.5.0 |
|
|
- Tokenizers: 0.21.0 |
|
|
|
|
|
## Citation |
|
|
|
|
|
### BibTeX |
|
|
|
|
|
#### Sentence Transformers |
|
|
```bibtex |
|
|
@inproceedings{reimers-2019-sentence-bert, |
|
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
|
month = "11", |
|
|
year = "2019", |
|
|
publisher = "Association for Computational Linguistics", |
|
|
url = "https://arxiv.org/abs/1908.10084", |
|
|
} |
|
|
``` |
|
|
|
|
|
#### MultipleNegativesRankingLoss |
|
|
```bibtex |
|
|
@misc{henderson2017efficient, |
|
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
|
year={2017}, |
|
|
eprint={1705.00652}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.CL} |
|
|
} |
|
|
``` |
|
|
|
|
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