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SentenceTransformer based on google-t5/t5-base

Name: t5-base-no-mrl
Availability: InStock

This is a sentence-transformers model finetuned from google-t5/t5-base on the all-nli dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: google-t5/t5-base
Maximum Sequence Length: None tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- all-nli
Language: en

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': None, 'do_lower_case': False, 'architecture': 'T5EncoderModel'})
  (1): Pooling({'word_embedding_dimension': 768, '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})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.',
    'A worker is looking out of a manhole.',
    'The workers are both inside the manhole.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Semantic Similarity

Datasets: sts-dev and sts-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	sts-dev	sts-test
pearson_cosine	0.845	0.8327
spearman_cosine	0.8464	0.8431

Training Details

Training Dataset

all-nli

Dataset: all-nli at d482672
Size: 557,850 training samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 1000 samples:
anchor positive negative
type string string string
details
min: 6 tokens
mean: 9.96 tokens
max: 52 tokens
min: 5 tokens
mean: 12.79 tokens
max: 44 tokens
min: 4 tokens
mean: 14.02 tokens
max: 57 tokens

Samples:

anchor	positive	negative
`A person on a horse jumps over a broken down airplane.`	`A person is outdoors, on a horse.`	`A person is at a diner, ordering an omelette.`
`Children smiling and waving at camera`	`There are children present`	`The kids are frowning`
`A boy is jumping on skateboard in the middle of a red bridge.`	`The boy does a skateboarding trick.`	`The boy skates down the sidewalk.`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 768 5 ], 6 "matryoshka_weights": [ 7 1 8 ], 9 "n_dims_per_step": -1 10}

Evaluation Dataset

all-nli

Dataset: all-nli at d482672
Size: 6,584 evaluation samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 1000 samples:
anchor positive negative
type string string string
details
min: 5 tokens
mean: 19.41 tokens
max: 79 tokens
min: 4 tokens
mean: 9.69 tokens
max: 35 tokens
min: 4 tokens
mean: 10.35 tokens
max: 30 tokens

Samples:

anchor	positive	negative
`Two women are embracing while holding to go packages.`	`Two woman are holding packages.`	`The men are fighting outside a deli.`
`Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.`	`Two kids in numbered jerseys wash their hands.`	`Two kids in jackets walk to school.`
`A man selling donuts to a customer during a world exhibition event held in the city of Angeles`	`A man selling donuts to a customer.`	`A woman drinks her coffee in a small cafe.`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 768 5 ], 6 "matryoshka_weights": [ 7 1 8 ], 9 "n_dims_per_step": -1 10}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
num_train_epochs: 15
warmup_ratio: 0.1

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 15
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
bf16: False
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
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}
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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
hub_revision: None
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: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	sts-dev_spearman_cosine	sts-test_spearman_cosine
-1	-1	-	-	0.6510	-
0.0287	500	2.5308	1.3756	0.6760	-
0.0574	1000	2.1199	1.0631	0.7312	-
0.0860	1500	1.6381	0.8444	0.7651	-
0.1147	2000	1.3407	0.7479	0.7763	-
0.1434	2500	1.166	0.6835	0.7835	-
0.1721	3000	1.0809	0.6264	0.7883	-
0.2008	3500	1.0059	0.5808	0.7916	-
0.2294	4000	0.9292	0.5431	0.7959	-
0.2581	4500	0.8938	0.5134	0.7996	-
0.2868	5000	0.843	0.4859	0.8032	-
0.3155	5500	0.7958	0.4636	0.8064	-
0.3442	6000	0.7612	0.4427	0.8105	-
0.3729	6500	0.7471	0.4230	0.8132	-
0.4015	7000	0.7155	0.4045	0.8146	-
0.4302	7500	0.6817	0.3918	0.8169	-
0.4589	8000	0.633	0.3781	0.8197	-
0.4876	8500	0.6551	0.3675	0.8206	-
0.5163	9000	0.6403	0.3585	0.8213	-
0.5449	9500	0.6128	0.3475	0.8229	-
0.5736	10000	0.5791	0.3394	0.8250	-
0.6023	10500	0.5819	0.3298	0.8257	-
0.6310	11000	0.559	0.3249	0.8286	-
0.6597	11500	0.5545	0.3163	0.8273	-
0.6883	12000	0.5377	0.3094	0.8314	-
0.7170	12500	0.5308	0.3029	0.8291	-
0.7457	13000	0.5203	0.2968	0.8304	-
0.7744	13500	0.5244	0.2946	0.8284	-
0.8031	14000	0.5046	0.2893	0.8294	-
0.8318	14500	0.4901	0.2843	0.8292	-
0.8604	15000	0.4917	0.2810	0.8319	-
0.8891	15500	0.4915	0.2757	0.8350	-
0.9178	16000	0.4834	0.2711	0.8343	-
0.9465	16500	0.4731	0.2692	0.8343	-
0.9752	17000	0.4547	0.2671	0.8353	-
1.0038	17500	0.4487	0.2640	0.8399	-
1.0325	18000	0.4331	0.2658	0.8368	-
1.0612	18500	0.4304	0.2654	0.8350	-
1.0899	19000	0.429	0.2603	0.8366	-
1.1186	19500	0.4207	0.2591	0.8386	-
1.1472	20000	0.4236	0.2570	0.8425	-
1.1759	20500	0.4072	0.2527	0.8435	-
1.2046	21000	0.4196	0.2533	0.8430	-
1.2333	21500	0.4117	0.2503	0.8417	-
1.2620	22000	0.3988	0.2486	0.8446	-
1.2907	22500	0.4016	0.2465	0.8453	-
1.3193	23000	0.398	0.2428	0.8438	-
1.3480	23500	0.3993	0.2491	0.8462	-
1.3767	24000	0.3893	0.2446	0.8452	-
1.4054	24500	0.3784	0.2414	0.8482	-
1.4341	25000	0.3789	0.2411	0.8464	-
1.4627	25500	0.3822	0.2368	0.8457	-
1.4914	26000	0.3736	0.2371	0.8456	-
1.5201	26500	0.3598	0.2358	0.8446	-
1.5488	27000	0.3525	0.2368	0.8469	-
1.5775	27500	0.3747	0.2307	0.8478	-
1.6061	28000	0.3649	0.2357	0.8486	-
1.6348	28500	0.355	0.2327	0.8475	-
1.6635	29000	0.3501	0.2347	0.8446	-
1.6922	29500	0.347	0.2263	0.8499	-
1.7209	30000	0.3565	0.2320	0.8490	-
1.7496	30500	0.3432	0.2271	0.8489	-
1.7782	31000	0.3409	0.2257	0.8483	-
1.8069	31500	0.3522	0.2261	0.8510	-
1.8356	32000	0.3348	0.2270	0.8504	-
1.8643	32500	0.339	0.2199	0.8495	-
1.8930	33000	0.333	0.2219	0.8502	-
1.9216	33500	0.3103	0.2208	0.8514	-
1.9503	34000	0.338	0.2240	0.8541	-
1.9790	34500	0.3276	0.2232	0.8548	-
2.0077	35000	0.3161	0.2248	0.8512	-
2.0364	35500	0.2905	0.2262	0.8528	-
2.0650	36000	0.2928	0.2216	0.8538	-
2.0937	36500	0.2961	0.2175	0.8515	-
2.1224	37000	0.2926	0.2184	0.8543	-
2.1511	37500	0.2923	0.2155	0.8466	-
2.1798	38000	0.2963	0.2201	0.8474	-
2.2085	38500	0.2853	0.2165	0.8492	-
2.2371	39000	0.2778	0.2212	0.8483	-
2.2658	39500	0.2889	0.2170	0.8532	-
2.2945	40000	0.2683	0.2201	0.8499	-
2.3232	40500	0.2812	0.2139	0.8548	-
2.3519	41000	0.2781	0.2193	0.8513	-
2.3805	41500	0.2848	0.2224	0.8482	-
2.4092	42000	0.2725	0.2232	0.8499	-
2.4379	42500	0.2727	0.2209	0.8525	-
2.4666	43000	0.2747	0.2254	0.8535	-
2.4953	43500	0.2785	0.2199	0.8528	-
2.5239	44000	0.2701	0.2181	0.8494	-
2.5526	44500	0.273	0.2195	0.8519	-
2.5813	45000	0.2763	0.2160	0.8540	-
2.6100	45500	0.2635	0.2140	0.8534	-
2.6387	46000	0.2722	0.2176	0.8529	-
2.6674	46500	0.2596	0.2136	0.8552	-
2.6960	47000	0.2627	0.2142	0.8524	-
2.7247	47500	0.2673	0.2174	0.8502	-
2.7534	48000	0.2582	0.2147	0.8510	-
2.7821	48500	0.256	0.2148	0.8514	-
2.8108	49000	0.2567	0.2122	0.8524	-
2.8394	49500	0.2502	0.2142	0.8526	-
2.8681	50000	0.2514	0.2132	0.8521	-
2.8968	50500	0.2603	0.2134	0.8496	-
2.9255	51000	0.2544	0.2131	0.8520	-
2.9542	51500	0.2526	0.2132	0.8511	-
2.9828	52000	0.2483	0.2131	0.8491	-
3.0115	52500	0.2361	0.2157	0.8522	-
3.0402	53000	0.2258	0.2122	0.8504	-
3.0689	53500	0.2248	0.2138	0.8498	-
3.0976	54000	0.2293	0.2164	0.8497	-
3.1263	54500	0.2328	0.2141	0.8501	-
3.1549	55000	0.2216	0.2109	0.8512	-
3.1836	55500	0.2256	0.2143	0.8529	-
3.2123	56000	0.2189	0.2126	0.8497	-
3.2410	56500	0.2264	0.2131	0.8492	-
3.2697	57000	0.2183	0.2129	0.8478	-
3.2983	57500	0.2155	0.2132	0.8515	-
3.3270	58000	0.2192	0.2133	0.8512	-
3.3557	58500	0.221	0.2132	0.8530	-
3.3844	59000	0.2139	0.2146	0.8522	-
3.4131	59500	0.2201	0.2139	0.8533	-
3.4417	60000	0.2058	0.2111	0.8505	-
3.4704	60500	0.2129	0.2116	0.8535	-
3.4991	61000	0.2137	0.2127	0.8513	-
3.5278	61500	0.2074	0.2129	0.8500	-
3.5565	62000	0.2157	0.2135	0.8506	-
3.5852	62500	0.2169	0.2153	0.8491	-
3.6138	63000	0.2052	0.2122	0.8527	-
3.6425	63500	0.2286	0.2144	0.8517	-
3.6712	64000	0.2105	0.2141	0.8491	-
3.6999	64500	0.2117	0.2147	0.8529	-
3.7286	65000	0.2117	0.2148	0.8510	-
3.7572	65500	0.2167	0.2158	0.8523	-
3.7859	66000	0.2176	0.2151	0.8506	-
3.8146	66500	0.2135	0.2131	0.8509	-
3.8433	67000	0.2148	0.2162	0.8532	-
3.8720	67500	0.2049	0.2128	0.8535	-
3.9006	68000	0.2133	0.2132	0.8511	-
3.9293	68500	0.2015	0.2177	0.8473	-
3.9580	69000	0.205	0.2165	0.8517	-
3.9867	69500	0.2032	0.2142	0.8548	-
4.0154	70000	0.1992	0.2173	0.8563	-
4.0441	70500	0.1851	0.2173	0.8541	-
4.0727	71000	0.1796	0.2154	0.8547	-
4.1014	71500	0.1836	0.2155	0.8544	-
4.1301	72000	0.181	0.2207	0.8513	-
4.1588	72500	0.1923	0.2198	0.8522	-
4.1875	73000	0.1865	0.2201	0.8533	-
4.2161	73500	0.1795	0.2139	0.8548	-
4.2448	74000	0.1813	0.2180	0.8512	-
4.2735	74500	0.1788	0.2147	0.8509	-
4.3022	75000	0.179	0.2133	0.8510	-
4.3309	75500	0.1796	0.2162	0.8519	-
4.3595	76000	0.1912	0.2185	0.8509	-
4.3882	76500	0.184	0.2162	0.8535	-
4.4169	77000	0.1827	0.2148	0.8535	-
4.4456	77500	0.1786	0.2146	0.8532	-
4.4743	78000	0.1826	0.2146	0.8534	-
4.5030	78500	0.1821	0.2165	0.8525	-
4.5316	79000	0.1781	0.2122	0.8524	-
4.5603	79500	0.1832	0.2147	0.8534	-
4.5890	80000	0.1812	0.2185	0.8534	-
4.6177	80500	0.1839	0.2163	0.8554	-
4.6464	81000	0.1834	0.2158	0.8542	-
4.6750	81500	0.1805	0.2171	0.8523	-
4.7037	82000	0.1818	0.2186	0.8536	-
4.7324	82500	0.1736	0.2176	0.8555	-
4.7611	83000	0.1754	0.2161	0.8525	-
4.7898	83500	0.1808	0.2187	0.8546	-
4.8184	84000	0.1794	0.2138	0.8543	-
4.8471	84500	0.1827	0.2147	0.8529	-
4.8758	85000	0.1773	0.2126	0.8535	-
4.9045	85500	0.1806	0.2124	0.8539	-
4.9332	86000	0.1843	0.2131	0.8490	-
4.9619	86500	0.1738	0.2120	0.8548	-
4.9905	87000	0.174	0.2120	0.8541	-
5.0192	87500	0.1644	0.2143	0.8533	-
5.0479	88000	0.1607	0.2132	0.8529	-
5.0766	88500	0.1539	0.2170	0.8504	-
5.1053	89000	0.1581	0.2123	0.8496	-
5.1339	89500	0.1563	0.2114	0.8519	-
5.1626	90000	0.158	0.2123	0.8494	-
5.1913	90500	0.1653	0.2147	0.8513	-
5.2200	91000	0.158	0.2170	0.8488	-
5.2487	91500	0.1559	0.2144	0.8489	-
5.2773	92000	0.165	0.2123	0.8498	-
5.3060	92500	0.15	0.2128	0.8497	-
5.3347	93000	0.1603	0.2126	0.8501	-
5.3634	93500	0.1584	0.2119	0.8490	-
5.3921	94000	0.1626	0.2121	0.8513	-
5.4208	94500	0.1585	0.2128	0.8505	-
5.4494	95000	0.1593	0.2099	0.8518	-
5.4781	95500	0.1546	0.2109	0.8482	-
5.5068	96000	0.1554	0.2115	0.8509	-
5.5355	96500	0.1628	0.2099	0.8507	-
5.5642	97000	0.1558	0.2124	0.8514	-
5.5928	97500	0.1556	0.2113	0.8510	-
5.6215	98000	0.1511	0.2091	0.8527	-
5.6502	98500	0.1545	0.2110	0.8518	-
5.6789	99000	0.1548	0.2113	0.8513	-
5.7076	99500	0.1556	0.2119	0.8516	-
5.7362	100000	0.1638	0.2113	0.8497	-
5.7649	100500	0.1516	0.2116	0.8500	-
5.7936	101000	0.1518	0.2124	0.8487	-
5.8223	101500	0.1562	0.2136	0.8485	-
5.8510	102000	0.1566	0.2132	0.8484	-
5.8797	102500	0.1517	0.2123	0.8487	-
5.9083	103000	0.1618	0.2114	0.8478	-
5.9370	103500	0.1531	0.2105	0.8487	-
5.9657	104000	0.1588	0.2106	0.8492	-
5.9944	104500	0.1563	0.2115	0.8486	-
6.0231	105000	0.151	0.2115	0.8505	-
6.0517	105500	0.1414	0.2111	0.8503	-
6.0804	106000	0.1316	0.2119	0.8491	-
6.1091	106500	0.1431	0.2108	0.8501	-
6.1378	107000	0.1336	0.2121	0.8508	-
6.1665	107500	0.1367	0.2111	0.8500	-
6.1951	108000	0.1397	0.2134	0.8496	-
6.2238	108500	0.1463	0.2128	0.8508	-
6.2525	109000	0.1416	0.2133	0.8525	-
6.2812	109500	0.147	0.2153	0.8504	-
6.3099	110000	0.1398	0.2133	0.8509	-
6.3386	110500	0.1451	0.2117	0.8534	-
6.3672	111000	0.1279	0.2129	0.8500	-
6.3959	111500	0.1309	0.2152	0.8484	-
6.4246	112000	0.1353	0.2128	0.8514	-
6.4533	112500	0.1355	0.2124	0.8501	-
6.4820	113000	0.1377	0.2141	0.8498	-
6.5106	113500	0.1376	0.2154	0.8522	-
6.5393	114000	0.1375	0.2148	0.8518	-
6.5680	114500	0.1348	0.2160	0.8507	-
6.5967	115000	0.1416	0.2139	0.8511	-
6.6254	115500	0.1396	0.2136	0.8503	-
6.6540	116000	0.1419	0.2130	0.8519	-
6.6827	116500	0.1435	0.2131	0.8527	-
6.7114	117000	0.1318	0.2128	0.8526	-
6.7401	117500	0.1362	0.2136	0.8530	-
6.7688	118000	0.1374	0.2125	0.8515	-
6.7975	118500	0.1384	0.2116	0.8528	-
6.8261	119000	0.1419	0.2122	0.8514	-
6.8548	119500	0.1303	0.2134	0.8506	-
6.8835	120000	0.1341	0.2125	0.8531	-
6.9122	120500	0.1342	0.2118	0.8512	-
6.9409	121000	0.1419	0.2107	0.8504	-
6.9695	121500	0.138	0.2108	0.8510	-
6.9982	122000	0.1318	0.2126	0.8525	-
7.0269	122500	0.1294	0.2138	0.8491	-
7.0556	123000	0.1355	0.2137	0.8504	-
7.0843	123500	0.1222	0.2148	0.8506	-
7.1129	124000	0.1231	0.2137	0.8530	-
7.1416	124500	0.1303	0.2142	0.8530	-
7.1703	125000	0.1281	0.2158	0.8515	-
7.1990	125500	0.1222	0.2134	0.8508	-
7.2277	126000	0.1316	0.2157	0.8512	-
7.2564	126500	0.1222	0.2151	0.8512	-
7.2850	127000	0.125	0.2147	0.8502	-
7.3137	127500	0.1271	0.2146	0.8508	-
7.3424	128000	0.1259	0.2167	0.8508	-
7.3711	128500	0.1318	0.2151	0.8525	-
7.3998	129000	0.1259	0.2182	0.8511	-
7.4284	129500	0.1249	0.2137	0.8541	-
7.4571	130000	0.1278	0.2177	0.8500	-
7.4858	130500	0.1246	0.2130	0.8507	-
7.5145	131000	0.1308	0.2107	0.8542	-
7.5432	131500	0.124	0.2107	0.8527	-
7.5718	132000	0.1196	0.2141	0.8505	-
7.6005	132500	0.1246	0.2116	0.8516	-
7.6292	133000	0.1269	0.2101	0.8534	-
7.6579	133500	0.1239	0.2110	0.8522	-
7.6866	134000	0.1344	0.2117	0.8512	-
7.7153	134500	0.129	0.2110	0.8515	-
7.7439	135000	0.1248	0.2113	0.8503	-
7.7726	135500	0.1261	0.2121	0.8501	-
7.8013	136000	0.1223	0.2110	0.8486	-
7.8300	136500	0.1236	0.2091	0.8494	-
7.8587	137000	0.1211	0.2084	0.8498	-
7.8873	137500	0.1187	0.2112	0.8473	-
7.9160	138000	0.1242	0.2090	0.8510	-
7.9447	138500	0.1206	0.2096	0.8503	-
7.9734	139000	0.1187	0.2125	0.8523	-
8.0021	139500	0.1242	0.2105	0.8497	-
8.0307	140000	0.1128	0.2134	0.8518	-
8.0594	140500	0.1188	0.2121	0.8509	-
8.0881	141000	0.1151	0.2133	0.8510	-
8.1168	141500	0.1213	0.2113	0.8508	-
8.1455	142000	0.1149	0.2126	0.8502	-
8.1742	142500	0.1126	0.2132	0.8528	-
8.2028	143000	0.1158	0.2129	0.8516	-
8.2315	143500	0.1188	0.2118	0.8510	-
8.2602	144000	0.1219	0.2115	0.8519	-
8.2889	144500	0.1184	0.2106	0.8522	-
8.3176	145000	0.1156	0.2102	0.8518	-
8.3462	145500	0.1125	0.2118	0.8513	-
8.3749	146000	0.1128	0.2103	0.8510	-
8.4036	146500	0.1118	0.2096	0.8502	-
8.4323	147000	0.1158	0.2080	0.8493	-
8.4610	147500	0.1172	0.2100	0.8477	-
8.4896	148000	0.1182	0.2153	0.8465	-
8.5183	148500	0.1173	0.2124	0.8483	-
8.5470	149000	0.1153	0.2118	0.8496	-
8.5757	149500	0.1172	0.2116	0.8487	-
8.6044	150000	0.114	0.2094	0.8516	-
8.6331	150500	0.1188	0.2117	0.8497	-
8.6617	151000	0.116	0.2128	0.8503	-
8.6904	151500	0.1152	0.2118	0.8505	-
8.7191	152000	0.1148	0.2147	0.8511	-
8.7478	152500	0.1136	0.2109	0.8514	-
8.7765	153000	0.1096	0.2104	0.8503	-
8.8051	153500	0.1151	0.2102	0.8509	-
8.8338	154000	0.1184	0.2129	0.8504	-
8.8625	154500	0.1156	0.2143	0.8503	-
8.8912	155000	0.1138	0.2121	0.8530	-
8.9199	155500	0.1144	0.2124	0.8527	-
8.9485	156000	0.1189	0.2136	0.8511	-
8.9772	156500	0.116	0.2139	0.8501	-
9.0059	157000	0.1132	0.2139	0.8495	-
9.0346	157500	0.1063	0.2135	0.8515	-
9.0633	158000	0.1069	0.2142	0.8501	-
9.0920	158500	0.0992	0.2163	0.8497	-
9.1206	159000	0.0995	0.2133	0.8534	-
9.1493	159500	0.1047	0.2144	0.8509	-
9.1780	160000	0.1074	0.2137	0.8516	-
9.2067	160500	0.1084	0.2169	0.8498	-
9.2354	161000	0.1081	0.2143	0.8505	-
9.2640	161500	0.1048	0.2141	0.8520	-
9.2927	162000	0.1055	0.2162	0.8489	-
9.3214	162500	0.101	0.2163	0.8485	-
9.3501	163000	0.1036	0.2153	0.8481	-
9.3788	163500	0.1057	0.2153	0.8489	-
9.4074	164000	0.1075	0.2150	0.8493	-
9.4361	164500	0.1055	0.2159	0.8500	-
9.4648	165000	0.1043	0.2152	0.8503	-
9.4935	165500	0.1072	0.2161	0.8513	-
9.5222	166000	0.1041	0.2154	0.8505	-
9.5509	166500	0.1064	0.2165	0.8505	-
9.5795	167000	0.1062	0.2168	0.8511	-
9.6082	167500	0.1046	0.2163	0.8494	-
9.6369	168000	0.1064	0.2167	0.8492	-
9.6656	168500	0.1017	0.2167	0.8499	-
9.6943	169000	0.1015	0.2143	0.8485	-
9.7229	169500	0.1075	0.2163	0.8477	-
9.7516	170000	0.1032	0.2176	0.8463	-
9.7803	170500	0.1115	0.2158	0.8471	-
9.8090	171000	0.1073	0.2127	0.8475	-
9.8377	171500	0.1056	0.2145	0.8481	-
9.8663	172000	0.108	0.2150	0.8488	-
9.8950	172500	0.1089	0.2142	0.8482	-
9.9237	173000	0.1037	0.2152	0.8475	-
9.9524	173500	0.1062	0.2137	0.8474	-
9.9811	174000	0.1058	0.2159	0.8482	-
10.0098	174500	0.101	0.2132	0.8481	-
10.0384	175000	0.1005	0.2152	0.8505	-
10.0671	175500	0.0963	0.2166	0.8475	-
10.0958	176000	0.1014	0.2150	0.8471	-
10.1245	176500	0.1012	0.2165	0.8467	-
10.1532	177000	0.1041	0.2144	0.8475	-
10.1818	177500	0.0982	0.2129	0.8483	-
10.2105	178000	0.098	0.2175	0.8463	-
10.2392	178500	0.0984	0.2127	0.8492	-
10.2679	179000	0.1	0.2148	0.8464	-
10.2966	179500	0.0985	0.2125	0.8468	-
10.3252	180000	0.1032	0.2102	0.8480	-
10.3539	180500	0.1019	0.2155	0.8455	-
10.3826	181000	0.1025	0.2105	0.8485	-
10.4113	181500	0.0987	0.2144	0.8471	-
10.4400	182000	0.1016	0.2142	0.8453	-
10.4687	182500	0.0981	0.2154	0.8466	-
10.4973	183000	0.0971	0.2150	0.8463	-
10.5260	183500	0.098	0.2136	0.8467	-
10.5547	184000	0.0995	0.2150	0.8469	-
10.5834	184500	0.0993	0.2134	0.8491	-
10.6121	185000	0.0983	0.2128	0.8483	-
10.6407	185500	0.1033	0.2143	0.8475	-
10.6694	186000	0.094	0.2138	0.8484	-
10.6981	186500	0.1026	0.2136	0.8477	-
10.7268	187000	0.1012	0.2140	0.8486	-
10.7555	187500	0.0926	0.2161	0.8481	-
10.7841	188000	0.1041	0.2134	0.8482	-
10.8128	188500	0.094	0.2150	0.8471	-
10.8415	189000	0.104	0.2157	0.8467	-
10.8702	189500	0.1015	0.2139	0.8472	-
10.8989	190000	0.0942	0.2173	0.8473	-
10.9276	190500	0.1002	0.2168	0.8471	-
10.9562	191000	0.1038	0.2169	0.8472	-
10.9849	191500	0.1026	0.2157	0.8463	-
11.0136	192000	0.0975	0.2161	0.8471	-
11.0423	192500	0.0918	0.2146	0.8476	-
11.0710	193000	0.0962	0.2172	0.8469	-
11.0996	193500	0.0928	0.2172	0.8472	-
11.1283	194000	0.0936	0.2165	0.8478	-
11.1570	194500	0.0875	0.2191	0.8472	-
11.1857	195000	0.0997	0.2190	0.8478	-
11.2144	195500	0.0937	0.2215	0.8455	-
11.2430	196000	0.0971	0.2168	0.8458	-
11.2717	196500	0.0963	0.2170	0.8456	-
11.3004	197000	0.0922	0.2183	0.8463	-
11.3291	197500	0.0946	0.2175	0.8448	-
11.3578	198000	0.0976	0.2172	0.8445	-
11.3865	198500	0.0918	0.2171	0.8457	-
11.4151	199000	0.1029	0.2165	0.8459	-
11.4438	199500	0.0949	0.2154	0.8475	-
11.4725	200000	0.0937	0.2172	0.8446	-
11.5012	200500	0.096	0.2181	0.8459	-
11.5299	201000	0.0957	0.2190	0.8451	-
11.5585	201500	0.0988	0.2164	0.8455	-
11.5872	202000	0.0966	0.2166	0.8443	-
11.6159	202500	0.0922	0.2168	0.8440	-
11.6446	203000	0.0914	0.2167	0.8452	-
11.6733	203500	0.0935	0.2153	0.8455	-
11.7019	204000	0.0946	0.2161	0.8455	-
11.7306	204500	0.0969	0.2159	0.8465	-
11.7593	205000	0.0956	0.2166	0.8448	-
11.7880	205500	0.0892	0.2150	0.8455	-
11.8167	206000	0.0919	0.2162	0.8459	-
11.8454	206500	0.0975	0.2162	0.8464	-
11.8740	207000	0.0925	0.2169	0.8454	-
11.9027	207500	0.0883	0.2169	0.8459	-
11.9314	208000	0.0957	0.2160	0.8468	-
11.9601	208500	0.0941	0.2162	0.8471	-
11.9888	209000	0.0924	0.2175	0.8465	-
12.0174	209500	0.0895	0.2159	0.8469	-
12.0461	210000	0.0877	0.2168	0.8457	-
12.0748	210500	0.0908	0.2162	0.8460	-
12.1035	211000	0.0907	0.2174	0.8461	-
12.1322	211500	0.0893	0.2179	0.8450	-
12.1608	212000	0.0868	0.2180	0.8452	-
12.1895	212500	0.0924	0.2180	0.8466	-
12.2182	213000	0.0888	0.2167	0.8462	-
12.2469	213500	0.0846	0.2167	0.8452	-
12.2756	214000	0.0921	0.2166	0.8458	-
12.3043	214500	0.0854	0.2176	0.8456	-
12.3329	215000	0.0877	0.2154	0.8452	-
12.3616	215500	0.0933	0.2162	0.8455	-
12.3903	216000	0.0849	0.2191	0.8449	-
12.4190	216500	0.0889	0.2195	0.8437	-
12.4477	217000	0.0886	0.2183	0.8449	-
12.4763	217500	0.0907	0.2175	0.8464	-
12.5050	218000	0.0915	0.2171	0.8458	-
12.5337	218500	0.0908	0.2180	0.8465	-
12.5624	219000	0.0863	0.2192	0.8450	-
12.5911	219500	0.086	0.2190	0.8460	-
12.6197	220000	0.0944	0.2190	0.8462	-
12.6484	220500	0.0858	0.2186	0.8459	-
12.6771	221000	0.0918	0.2176	0.8466	-
12.7058	221500	0.0934	0.2185	0.8468	-
12.7345	222000	0.0903	0.2182	0.8472	-
12.7632	222500	0.0858	0.2179	0.8467	-
12.7918	223000	0.0941	0.2188	0.8461	-
12.8205	223500	0.0867	0.2170	0.8464	-
12.8492	224000	0.0881	0.2173	0.8471	-
12.8779	224500	0.0869	0.2185	0.8464	-
12.9066	225000	0.0933	0.2181	0.8467	-
12.9352	225500	0.0923	0.2177	0.8464	-
12.9639	226000	0.0887	0.2175	0.8471	-
12.9926	226500	0.0958	0.2180	0.8472	-
13.0213	227000	0.085	0.2181	0.8463	-
13.0500	227500	0.0818	0.2169	0.8467	-
13.0786	228000	0.0876	0.2186	0.8459	-
13.1073	228500	0.0913	0.2190	0.8453	-
13.1360	229000	0.0853	0.2193	0.8454	-
13.1647	229500	0.0886	0.2199	0.8463	-
13.1934	230000	0.085	0.2202	0.8465	-
13.2221	230500	0.0879	0.2222	0.8453	-
13.2507	231000	0.0853	0.2208	0.8457	-
13.2794	231500	0.0831	0.2192	0.8459	-
13.3081	232000	0.0865	0.2202	0.8455	-
13.3368	232500	0.091	0.2209	0.8449	-
13.3655	233000	0.0848	0.2193	0.8454	-
13.3941	233500	0.0873	0.2192	0.8453	-
13.4228	234000	0.0813	0.2197	0.8449	-
13.4515	234500	0.0883	0.2205	0.8447	-
13.4802	235000	0.0858	0.2193	0.8463	-
13.5089	235500	0.0902	0.2197	0.8466	-
13.5375	236000	0.0837	0.2185	0.8469	-
13.5662	236500	0.0922	0.2201	0.8462	-
13.5949	237000	0.0876	0.2197	0.8463	-
13.6236	237500	0.0839	0.2191	0.8458	-
13.6523	238000	0.0878	0.2197	0.8454	-
13.6809	238500	0.0874	0.2197	0.8451	-
13.7096	239000	0.0848	0.2198	0.8457	-
13.7383	239500	0.0842	0.2185	0.8459	-
13.7670	240000	0.0827	0.2184	0.8463	-
13.7957	240500	0.0885	0.2176	0.8458	-
13.8244	241000	0.0872	0.2180	0.8462	-
13.8530	241500	0.0856	0.2180	0.8468	-
13.8817	242000	0.0887	0.2184	0.8459	-
13.9104	242500	0.0875	0.2187	0.8461	-
13.9391	243000	0.0857	0.2195	0.8460	-
13.9678	243500	0.0845	0.2188	0.8467	-
13.9964	244000	0.0896	0.2184	0.8463	-
14.0251	244500	0.0818	0.2189	0.8467	-
14.0538	245000	0.09	0.2194	0.8460	-
14.0825	245500	0.0842	0.2190	0.8456	-
14.1112	246000	0.0878	0.2190	0.8460	-
14.1398	246500	0.0838	0.2195	0.8462	-
14.1685	247000	0.0781	0.2201	0.8460	-
14.1972	247500	0.0847	0.2193	0.8466	-
14.2259	248000	0.0881	0.2188	0.8470	-
14.2546	248500	0.082	0.2184	0.8473	-
14.2833	249000	0.0886	0.2191	0.8469	-
14.3119	249500	0.0874	0.2195	0.8470	-
14.3406	250000	0.0833	0.2197	0.8465	-
14.3693	250500	0.0856	0.2197	0.8461	-
14.3980	251000	0.0834	0.2198	0.8464	-
14.4267	251500	0.0852	0.2199	0.8461	-
14.4553	252000	0.0853	0.2201	0.8456	-
14.4840	252500	0.0811	0.2197	0.8461	-
14.5127	253000	0.0778	0.2195	0.8464	-
14.5414	253500	0.0837	0.2200	0.8462	-
14.5701	254000	0.0835	0.2203	0.8459	-
14.5987	254500	0.0854	0.2199	0.8462	-
14.6274	255000	0.0877	0.2196	0.8464	-
14.6561	255500	0.0826	0.2198	0.8463	-
14.6848	256000	0.0894	0.2197	0.8463	-
14.7135	256500	0.0873	0.2199	0.8462	-
14.7422	257000	0.0818	0.2197	0.8462	-
14.7708	257500	0.0854	0.2196	0.8464	-
14.7995	258000	0.0823	0.2195	0.8464	-
14.8282	258500	0.0769	0.2194	0.8465	-
14.8569	259000	0.0842	0.2195	0.8465	-
14.8856	259500	0.0848	0.2195	0.8464	-
14.9142	260000	0.0839	0.2196	0.8464	-
14.9429	260500	0.089	0.2196	0.8464	-
14.9716	261000	0.0881	0.2197	0.8464	-
-1	-1	-	-	-	0.8431

Framework Versions

Python: 3.13.0
Sentence Transformers: 5.1.2
Transformers: 4.57.1
PyTorch: 2.9.1+cu128
Accelerate: 1.11.0
Datasets: 4.4.1
Tokenizers: 0.22.1

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",
}

MatryoshkaLoss

bibtex

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

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}
}

	anchor	positive	negative
type	string	string	string
details	min: 6 tokens mean: 9.96 tokens max: 52 tokens	min: 5 tokens mean: 12.79 tokens max: 44 tokens	min: 4 tokens mean: 14.02 tokens max: 57 tokens

sobamchan/t5-base-no-mrl

SentenceTransformer based on google-t5/t5-base

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Semantic Similarity

Training Details

Training Dataset

all-nli

Evaluation Dataset

all-nli

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MatryoshkaLoss

MultipleNegativesRankingLoss

sobamchan/
t5-base-no-mrl