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SentenceTransformer based on FacebookAI/roberta-base

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

This is a sentence-transformers model finetuned from FacebookAI/roberta-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: FacebookAI/roberta-base
Maximum Sequence Length: 512 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': 512, 'do_lower_case': False, 'architecture': 'RobertaModel'})
  (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)
# tensor([[1.0000, 0.5999, 0.2681],
#         [0.5999, 1.0000, 0.3853],
#         [0.2681, 0.3853, 1.0000]])

Evaluation

Metrics

Semantic Similarity

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

Metric	sts-dev	sts-test
pearson_cosine	0.8219	0.8
spearman_cosine	0.8227	0.8024

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: 7 tokens
mean: 10.38 tokens
max: 45 tokens
min: 6 tokens
mean: 12.8 tokens
max: 39 tokens
min: 6 tokens
mean: 13.4 tokens
max: 50 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: 6 tokens
mean: 18.02 tokens
max: 66 tokens
min: 5 tokens
mean: 9.81 tokens
max: 29 tokens
min: 5 tokens
mean: 10.37 tokens
max: 29 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	-	-	0.6500	-
0.0287	500	2.7438	0.7773	0.8130	-
0.0574	1000	0.9358	0.4743	0.8453	-
0.0860	1500	0.7553	0.3828	0.8462	-
0.1147	2000	0.659	0.3337	0.8509	-
0.1434	2500	0.5918	0.3060	0.8518	-
0.1721	3000	0.5592	0.2915	0.8599	-
0.2008	3500	0.5335	0.2755	0.8534	-
0.2294	4000	0.4947	0.2688	0.8548	-
0.2581	4500	0.4888	0.2614	0.8633	-
0.2868	5000	0.4671	0.2511	0.8608	-
0.3155	5500	0.4463	0.2397	0.8630	-
0.3442	6000	0.4484	0.2431	0.8700	-
0.3729	6500	0.449	0.2439	0.8608	-
0.4015	7000	0.4401	0.2356	0.8611	-
0.4302	7500	0.414	0.2388	0.8545	-
0.4589	8000	0.3999	0.2299	0.8562	-
0.4876	8500	0.4197	0.2309	0.8578	-
0.5163	9000	0.4208	0.2282	0.8521	-
0.5449	9500	0.4003	0.2330	0.8612	-
0.5736	10000	0.3802	0.2392	0.8533	-
0.6023	10500	0.3898	0.2273	0.8502	-
0.6310	11000	0.3722	0.2333	0.8550	-
0.6597	11500	0.383	0.2202	0.8575	-
0.6883	12000	0.3638	0.2267	0.8515	-
0.7170	12500	0.3694	0.2353	0.8548	-
0.7457	13000	0.3702	0.2239	0.8501	-
0.7744	13500	0.3605	0.2227	0.8464	-
0.8031	14000	0.3495	0.2290	0.8537	-
0.8318	14500	0.3538	0.2301	0.8625	-
0.8604	15000	0.3532	0.2256	0.8516	-
0.8891	15500	0.3552	0.2241	0.8563	-
0.9178	16000	0.3491	0.2212	0.8550	-
0.9465	16500	0.3421	0.2293	0.8467	-
0.9752	17000	0.3281	0.2239	0.8463	-
1.0038	17500	0.3274	0.2280	0.8433	-
1.0325	18000	0.2927	0.2325	0.8446	-
1.0612	18500	0.2966	0.2388	0.8404	-
1.0899	19000	0.3004	0.2261	0.8457	-
1.1186	19500	0.3019	0.2201	0.8456	-
1.1472	20000	0.3065	0.2502	0.8494	-
1.1759	20500	0.3001	0.2349	0.8453	-
1.2046	21000	0.305	0.2388	0.8472	-
1.2333	21500	0.3124	0.2303	0.8372	-
1.2620	22000	0.294	0.2300	0.8485	-
1.2907	22500	0.2935	0.2479	0.8434	-
1.3193	23000	0.299	0.2386	0.8426	-
1.3480	23500	0.3066	0.2418	0.8369	-
1.3767	24000	0.3032	0.2497	0.8398	-
1.4054	24500	0.2989	0.2370	0.8380	-
1.4341	25000	0.2902	0.2432	0.8463	-
1.4627	25500	0.2958	0.2546	0.8359	-
1.4914	26000	0.2976	0.2477	0.8477	-
1.5201	26500	0.2906	0.2698	0.8435	-
1.5488	27000	0.2881	0.2490	0.8349	-
1.5775	27500	0.3108	0.2457	0.8369	-
1.6061	28000	0.2947	0.2507	0.8447	-
1.6348	28500	0.2973	0.2397	0.8477	-
1.6635	29000	0.2895	0.2437	0.8378	-
1.6922	29500	0.2928	0.2434	0.8416	-
1.7209	30000	0.2926	0.2374	0.8420	-
1.7496	30500	0.2834	0.2360	0.8425	-
1.7782	31000	0.2822	0.2384	0.8448	-
1.8069	31500	0.2914	0.2350	0.8433	-
1.8356	32000	0.2769	0.2425	0.8415	-
1.8643	32500	0.2823	0.2348	0.8432	-
1.8930	33000	0.2764	0.2450	0.8520	-
1.9216	33500	0.2612	0.2416	0.8414	-
1.9503	34000	0.2762	0.2400	0.8454	-
1.9790	34500	0.27	0.2502	0.8410	-
2.0077	35000	0.2552	0.2392	0.8445	-
2.0364	35500	0.206	0.2455	0.8422	-
2.0650	36000	0.2233	0.2389	0.8440	-
2.0937	36500	0.2182	0.2389	0.8442	-
2.1224	37000	0.2213	0.2311	0.8428	-
2.1511	37500	0.2138	0.2537	0.8278	-
2.1798	38000	0.2208	0.2267	0.8366	-
2.2085	38500	0.2111	0.2329	0.8450	-
2.2371	39000	0.2026	0.2418	0.8484	-
2.2658	39500	0.2187	0.2387	0.8491	-
2.2945	40000	0.2131	0.2398	0.8376	-
2.3232	40500	0.2127	0.2395	0.8465	-
2.3519	41000	0.2083	0.2422	0.8393	-
2.3805	41500	0.2164	0.2391	0.8441	-
2.4092	42000	0.206	0.2408	0.8365	-
2.4379	42500	0.2033	0.2405	0.8391	-
2.4666	43000	0.2116	0.2477	0.8433	-
2.4953	43500	0.2046	0.2239	0.8330	-
2.5239	44000	0.2058	0.2346	0.8366	-
2.5526	44500	0.204	0.2347	0.8430	-
2.5813	45000	0.2053	0.2368	0.8393	-
2.6100	45500	0.1985	0.2470	0.8359	-
2.6387	46000	0.2083	0.2194	0.8352	-
2.6674	46500	0.1997	0.2271	0.8458	-
2.6960	47000	0.196	0.2396	0.8343	-
2.7247	47500	0.2064	0.2398	0.8281	-
2.7534	48000	0.2001	0.2357	0.8348	-
2.7821	48500	0.1894	0.2381	0.8302	-
2.8108	49000	0.1944	0.2332	0.8264	-
2.8394	49500	0.1962	0.2360	0.8298	-
2.8681	50000	0.1982	0.2315	0.8325	-
2.8968	50500	0.1946	0.2244	0.8411	-
2.9255	51000	0.1989	0.2321	0.8429	-
2.9542	51500	0.1946	0.2360	0.8424	-
2.9828	52000	0.1905	0.2206	0.8440	-
3.0115	52500	0.1746	0.2317	0.8348	-
3.0402	53000	0.1539	0.2303	0.8448	-
3.0689	53500	0.1535	0.2292	0.8351	-
3.0976	54000	0.1541	0.2324	0.8444	-
3.1263	54500	0.1562	0.2325	0.8372	-
3.1549	55000	0.1547	0.2266	0.8422	-
3.1836	55500	0.1598	0.2357	0.8415	-
3.2123	56000	0.1503	0.2433	0.8367	-
3.2410	56500	0.1526	0.2490	0.8373	-
3.2697	57000	0.1544	0.2301	0.8402	-
3.2983	57500	0.1483	0.2358	0.8417	-
3.3270	58000	0.1484	0.2457	0.8402	-
3.3557	58500	0.153	0.2359	0.8440	-
3.3844	59000	0.1532	0.2381	0.8411	-
3.4131	59500	0.1542	0.2384	0.8369	-
3.4417	60000	0.1455	0.2382	0.8324	-
3.4704	60500	0.1462	0.2355	0.8388	-
3.4991	61000	0.1468	0.2436	0.8367	-
3.5278	61500	0.1452	0.2406	0.8394	-
3.5565	62000	0.1548	0.2395	0.8424	-
3.5852	62500	0.1556	0.2389	0.8421	-
3.6138	63000	0.1448	0.2390	0.8395	-
3.6425	63500	0.1632	0.2403	0.8483	-
3.6712	64000	0.1471	0.2436	0.8393	-
3.6999	64500	0.1498	0.2439	0.8427	-
3.7286	65000	0.1506	0.2312	0.8387	-
3.7572	65500	0.1583	0.2356	0.8394	-
3.7859	66000	0.1523	0.2376	0.8356	-
3.8146	66500	0.1523	0.2334	0.8383	-
3.8433	67000	0.1588	0.2368	0.8394	-
3.8720	67500	0.1496	0.2275	0.8464	-
3.9006	68000	0.1512	0.2343	0.8465	-
3.9293	68500	0.1433	0.2432	0.8354	-
3.9580	69000	0.1431	0.2470	0.8354	-
3.9867	69500	0.1517	0.2329	0.8476	-
4.0154	70000	0.1322	0.2446	0.8397	-
4.0441	70500	0.1175	0.2377	0.8476	-
4.0727	71000	0.1195	0.2473	0.8413	-
4.1014	71500	0.1191	0.2306	0.8420	-
4.1301	72000	0.1103	0.2419	0.8400	-
4.1588	72500	0.1279	0.2338	0.8449	-
4.1875	73000	0.1201	0.2384	0.8406	-
4.2161	73500	0.1148	0.2408	0.8426	-
4.2448	74000	0.1125	0.2397	0.8459	-
4.2735	74500	0.1181	0.2452	0.8461	-
4.3022	75000	0.116	0.2323	0.8467	-
4.3309	75500	0.1167	0.2445	0.8448	-
4.3595	76000	0.1206	0.2333	0.8452	-
4.3882	76500	0.124	0.2315	0.8415	-
4.4169	77000	0.1184	0.2365	0.8340	-
4.4456	77500	0.1143	0.2360	0.8386	-
4.4743	78000	0.119	0.2409	0.8374	-
4.5030	78500	0.1162	0.2390	0.8413	-
4.5316	79000	0.1158	0.2473	0.8400	-
4.5603	79500	0.1205	0.2384	0.8400	-
4.5890	80000	0.1157	0.2370	0.8446	-
4.6177	80500	0.1189	0.2338	0.8459	-
4.6464	81000	0.1216	0.2484	0.8328	-
4.6750	81500	0.1217	0.2333	0.8388	-
4.7037	82000	0.1217	0.2391	0.8352	-
4.7324	82500	0.117	0.2416	0.8403	-
4.7611	83000	0.1196	0.2345	0.8336	-
4.7898	83500	0.1206	0.2279	0.8408	-
4.8184	84000	0.1142	0.2383	0.8353	-
4.8471	84500	0.1212	0.2297	0.8392	-
4.8758	85000	0.1186	0.2337	0.8438	-
4.9045	85500	0.1213	0.2339	0.8420	-
4.9332	86000	0.1173	0.2250	0.8434	-
4.9619	86500	0.1163	0.2314	0.8404	-
4.9905	87000	0.114	0.2265	0.8353	-
5.0192	87500	0.1044	0.2379	0.8404	-
5.0479	88000	0.0974	0.2453	0.8361	-
5.0766	88500	0.095	0.2465	0.8374	-
5.1053	89000	0.0925	0.2358	0.8383	-
5.1339	89500	0.0918	0.2368	0.8414	-
5.1626	90000	0.092	0.2401	0.8312	-
5.1913	90500	0.0976	0.2378	0.8380	-
5.2200	91000	0.0915	0.2344	0.8316	-
5.2487	91500	0.0913	0.2308	0.8334	-
5.2773	92000	0.0953	0.2281	0.8327	-
5.3060	92500	0.0928	0.2436	0.8339	-
5.3347	93000	0.0982	0.2420	0.8311	-
5.3634	93500	0.0951	0.2441	0.8307	-
5.3921	94000	0.0976	0.2437	0.8419	-
5.4208	94500	0.092	0.2431	0.8325	-
5.4494	95000	0.094	0.2364	0.8343	-
5.4781	95500	0.095	0.2379	0.8327	-
5.5068	96000	0.0926	0.2385	0.8383	-
5.5355	96500	0.098	0.2397	0.8380	-
5.5642	97000	0.0938	0.2381	0.8435	-
5.5928	97500	0.0969	0.2402	0.8377	-
5.6215	98000	0.0909	0.2341	0.8401	-
5.6502	98500	0.0946	0.2287	0.8400	-
5.6789	99000	0.0936	0.2345	0.8376	-
5.7076	99500	0.0971	0.2304	0.8422	-
5.7362	100000	0.0999	0.2357	0.8371	-
5.7649	100500	0.0882	0.2356	0.8376	-
5.7936	101000	0.089	0.2410	0.8408	-
5.8223	101500	0.0979	0.2431	0.8441	-
5.8510	102000	0.0947	0.2304	0.8450	-
5.8797	102500	0.0898	0.2304	0.8389	-
5.9083	103000	0.1036	0.2246	0.8384	-
5.9370	103500	0.0902	0.2328	0.8396	-
5.9657	104000	0.1048	0.2395	0.8403	-
5.9944	104500	0.093	0.2354	0.8380	-
6.0231	105000	0.0834	0.2423	0.8416	-
6.0517	105500	0.0749	0.2409	0.8393	-
6.0804	106000	0.0732	0.2361	0.8421	-
6.1091	106500	0.0784	0.2410	0.8423	-
6.1378	107000	0.0714	0.2472	0.8387	-
6.1665	107500	0.0743	0.2330	0.8388	-
6.1951	108000	0.0781	0.2440	0.8365	-
6.2238	108500	0.0805	0.2508	0.8380	-
6.2525	109000	0.0788	0.2376	0.8386	-
6.2812	109500	0.0826	0.2449	0.8357	-
6.3099	110000	0.0829	0.2299	0.8357	-
6.3386	110500	0.0831	0.2395	0.8366	-
6.3672	111000	0.0705	0.2343	0.8384	-
6.3959	111500	0.0755	0.2399	0.8345	-
6.4246	112000	0.0761	0.2389	0.8369	-
6.4533	112500	0.0763	0.2305	0.8395	-
6.4820	113000	0.0778	0.2361	0.8349	-
6.5106	113500	0.0773	0.2410	0.8338	-
6.5393	114000	0.0796	0.2420	0.8390	-
6.5680	114500	0.0763	0.2371	0.8361	-
6.5967	115000	0.0795	0.2323	0.8384	-
6.6254	115500	0.0807	0.2403	0.8353	-
6.6540	116000	0.0836	0.2394	0.8388	-
6.6827	116500	0.0848	0.2405	0.8308	-
6.7114	117000	0.073	0.2406	0.8339	-
6.7401	117500	0.0777	0.2396	0.8407	-
6.7688	118000	0.0778	0.2424	0.8302	-
6.7975	118500	0.0743	0.2461	0.8355	-
6.8261	119000	0.0781	0.2337	0.8322	-
6.8548	119500	0.0727	0.2450	0.8346	-
6.8835	120000	0.0732	0.2318	0.8340	-
6.9122	120500	0.0736	0.2437	0.8251	-
6.9409	121000	0.0801	0.2292	0.8297	-
6.9695	121500	0.0783	0.2398	0.8330	-
6.9982	122000	0.0773	0.2421	0.8307	-
7.0269	122500	0.0641	0.2350	0.8287	-
7.0556	123000	0.0683	0.2329	0.8327	-
7.0843	123500	0.0628	0.2310	0.8327	-
7.1129	124000	0.0643	0.2321	0.8321	-
7.1416	124500	0.0652	0.2438	0.8332	-
7.1703	125000	0.0698	0.2372	0.8304	-
7.1990	125500	0.0653	0.2414	0.8322	-
7.2277	126000	0.0688	0.2426	0.8331	-
7.2564	126500	0.061	0.2456	0.8297	-
7.2850	127000	0.0649	0.2384	0.8317	-
7.3137	127500	0.0623	0.2410	0.8331	-
7.3424	128000	0.0623	0.2426	0.8332	-
7.3711	128500	0.0694	0.2367	0.8358	-
7.3998	129000	0.0662	0.2356	0.8350	-
7.4284	129500	0.0658	0.2380	0.8355	-
7.4571	130000	0.0635	0.2376	0.8310	-
7.4858	130500	0.0675	0.2267	0.8331	-
7.5145	131000	0.0631	0.2379	0.8392	-
7.5432	131500	0.0661	0.2401	0.8331	-
7.5718	132000	0.0619	0.2417	0.8310	-
7.6005	132500	0.0611	0.2399	0.8320	-
7.6292	133000	0.0664	0.2383	0.8304	-
7.6579	133500	0.0643	0.2399	0.8313	-
7.6866	134000	0.0726	0.2444	0.8357	-
7.7153	134500	0.0643	0.2493	0.8292	-
7.7439	135000	0.0656	0.2425	0.8287	-
7.7726	135500	0.0645	0.2468	0.8322	-
7.8013	136000	0.0653	0.2340	0.8334	-
7.8300	136500	0.059	0.2440	0.8288	-
7.8587	137000	0.0626	0.2517	0.8308	-
7.8873	137500	0.0629	0.2393	0.8340	-
7.9160	138000	0.0651	0.2418	0.8376	-
7.9447	138500	0.0646	0.2315	0.8397	-
7.9734	139000	0.0605	0.2437	0.8309	-
8.0021	139500	0.0611	0.2385	0.8302	-
8.0307	140000	0.0552	0.2505	0.8338	-
8.0594	140500	0.055	0.2490	0.8275	-
8.0881	141000	0.0543	0.2519	0.8314	-
8.1168	141500	0.0577	0.2496	0.8311	-
8.1455	142000	0.0559	0.2467	0.8319	-
8.1742	142500	0.0515	0.2417	0.8322	-
8.2028	143000	0.0516	0.2497	0.8313	-
8.2315	143500	0.0574	0.2541	0.8288	-
8.2602	144000	0.059	0.2445	0.8318	-
8.2889	144500	0.0607	0.2442	0.8308	-
8.3176	145000	0.0573	0.2468	0.8320	-
8.3462	145500	0.0531	0.2439	0.8337	-
8.3749	146000	0.0539	0.2413	0.8343	-
8.4036	146500	0.0542	0.2388	0.8337	-
8.4323	147000	0.0564	0.2348	0.8314	-
8.4610	147500	0.0551	0.2373	0.8327	-
8.4896	148000	0.0585	0.2383	0.8350	-
8.5183	148500	0.0582	0.2427	0.8331	-
8.5470	149000	0.0586	0.2395	0.8331	-
8.5757	149500	0.0553	0.2389	0.8322	-
8.6044	150000	0.0528	0.2396	0.8295	-
8.6331	150500	0.0565	0.2414	0.8303	-
8.6617	151000	0.0611	0.2380	0.8329	-
8.6904	151500	0.0553	0.2352	0.8348	-
8.7191	152000	0.0547	0.2355	0.8334	-
8.7478	152500	0.0528	0.2336	0.8337	-
8.7765	153000	0.0511	0.2398	0.8286	-
8.8051	153500	0.0561	0.2365	0.8340	-
8.8338	154000	0.0551	0.2331	0.8314	-
8.8625	154500	0.0538	0.2320	0.8303	-
8.8912	155000	0.0542	0.2268	0.8315	-
8.9199	155500	0.0559	0.2267	0.8325	-
8.9485	156000	0.0535	0.2361	0.8267	-
8.9772	156500	0.0571	0.2325	0.8273	-
9.0059	157000	0.0546	0.2370	0.8268	-
9.0346	157500	0.0459	0.2359	0.8332	-
9.0633	158000	0.0477	0.2417	0.8330	-
9.0920	158500	0.043	0.2379	0.8344	-
9.1206	159000	0.0451	0.2412	0.8340	-
9.1493	159500	0.0497	0.2391	0.8331	-
9.1780	160000	0.0477	0.2355	0.8315	-
9.2067	160500	0.0502	0.2424	0.8307	-
9.2354	161000	0.0502	0.2404	0.8273	-
9.2640	161500	0.0425	0.2438	0.8338	-
9.2927	162000	0.0516	0.2416	0.8291	-
9.3214	162500	0.044	0.2393	0.8334	-
9.3501	163000	0.0466	0.2387	0.8315	-
9.3788	163500	0.0479	0.2375	0.8307	-
9.4074	164000	0.0458	0.2405	0.8315	-
9.4361	164500	0.0446	0.2391	0.8315	-
9.4648	165000	0.0467	0.2414	0.8299	-
9.4935	165500	0.048	0.2427	0.8319	-
9.5222	166000	0.0469	0.2388	0.8282	-
9.5509	166500	0.0486	0.2378	0.8321	-
9.5795	167000	0.0485	0.2380	0.8293	-
9.6082	167500	0.0439	0.2330	0.8280	-
9.6369	168000	0.0467	0.2356	0.8309	-
9.6656	168500	0.0453	0.2354	0.8291	-
9.6943	169000	0.0451	0.2358	0.8266	-
9.7229	169500	0.0463	0.2316	0.8275	-
9.7516	170000	0.0472	0.2331	0.8265	-
9.7803	170500	0.0485	0.2313	0.8290	-
9.8090	171000	0.0459	0.2373	0.8278	-
9.8377	171500	0.0453	0.2405	0.8266	-
9.8663	172000	0.0479	0.2377	0.8276	-
9.8950	172500	0.0445	0.2323	0.8317	-
9.9237	173000	0.0416	0.2438	0.8277	-
9.9524	173500	0.0472	0.2381	0.8268	-
9.9811	174000	0.0476	0.2355	0.8288	-
10.0098	174500	0.0423	0.2383	0.8263	-
10.0384	175000	0.0405	0.2405	0.8312	-
10.0671	175500	0.0394	0.2464	0.8311	-
10.0958	176000	0.0437	0.2471	0.8287	-
10.1245	176500	0.0442	0.2460	0.8275	-
10.1532	177000	0.047	0.2415	0.8286	-
10.1818	177500	0.0398	0.2467	0.8274	-
10.2105	178000	0.0421	0.2480	0.8263	-
10.2392	178500	0.0432	0.2528	0.8264	-
10.2679	179000	0.0442	0.2437	0.8290	-
10.2966	179500	0.04	0.2334	0.8353	-
10.3252	180000	0.0452	0.2379	0.8282	-
10.3539	180500	0.0438	0.2464	0.8274	-
10.3826	181000	0.0462	0.2439	0.8276	-
10.4113	181500	0.0405	0.2380	0.8275	-
10.4400	182000	0.0441	0.2379	0.8308	-
10.4687	182500	0.0406	0.2499	0.8239	-
10.4973	183000	0.0397	0.2457	0.8240	-
10.5260	183500	0.0387	0.2419	0.8272	-
10.5547	184000	0.0381	0.2453	0.8303	-
10.5834	184500	0.0403	0.2406	0.8294	-
10.6121	185000	0.0394	0.2376	0.8328	-
10.6407	185500	0.0419	0.2404	0.8266	-
10.6694	186000	0.0411	0.2345	0.8318	-
10.6981	186500	0.04	0.2416	0.8356	-
10.7268	187000	0.0428	0.2346	0.8379	-
10.7555	187500	0.04	0.2388	0.8368	-
10.7841	188000	0.0436	0.2426	0.8358	-
10.8128	188500	0.0393	0.2411	0.8340	-
10.8415	189000	0.0442	0.2381	0.8353	-
10.8702	189500	0.0421	0.2371	0.8318	-
10.8989	190000	0.0392	0.2465	0.8322	-
10.9276	190500	0.0407	0.2366	0.8331	-
10.9562	191000	0.0417	0.2359	0.8327	-
10.9849	191500	0.0452	0.2366	0.8305	-
11.0136	192000	0.0354	0.2429	0.8289	-
11.0423	192500	0.0346	0.2375	0.8291	-
11.0710	193000	0.0382	0.2379	0.8326	-
11.0996	193500	0.0375	0.2395	0.8333	-
11.1283	194000	0.0401	0.2399	0.8342	-
11.1570	194500	0.0321	0.2463	0.8328	-
11.1857	195000	0.0407	0.2455	0.8305	-
11.2144	195500	0.0406	0.2438	0.8334	-
11.2430	196000	0.0384	0.2447	0.8326	-
11.2717	196500	0.0381	0.2498	0.8304	-
11.3004	197000	0.037	0.2474	0.8330	-
11.3291	197500	0.0376	0.2484	0.8290	-
11.3578	198000	0.0394	0.2487	0.8295	-
11.3865	198500	0.0374	0.2469	0.8317	-
11.4151	199000	0.0409	0.2434	0.8321	-
11.4438	199500	0.0393	0.2399	0.8341	-
11.4725	200000	0.0363	0.2411	0.8295	-
11.5012	200500	0.0373	0.2435	0.8263	-
11.5299	201000	0.0364	0.2450	0.8263	-
11.5585	201500	0.041	0.2399	0.8283	-
11.5872	202000	0.0407	0.2403	0.8283	-
11.6159	202500	0.0368	0.2378	0.8286	-
11.6446	203000	0.0345	0.2333	0.8321	-
11.6733	203500	0.0366	0.2420	0.8289	-
11.7019	204000	0.0372	0.2334	0.8289	-
11.7306	204500	0.0382	0.2359	0.8285	-
11.7593	205000	0.0371	0.2392	0.8274	-
11.7880	205500	0.0308	0.2378	0.8276	-
11.8167	206000	0.0362	0.2420	0.8253	-
11.8454	206500	0.0373	0.2396	0.8261	-
11.8740	207000	0.0346	0.2369	0.8279	-
11.9027	207500	0.0335	0.2344	0.8319	-
11.9314	208000	0.0393	0.2366	0.8281	-
11.9601	208500	0.0365	0.2393	0.8302	-
11.9888	209000	0.0332	0.2354	0.8303	-
12.0174	209500	0.0344	0.2360	0.8278	-
12.0461	210000	0.0317	0.2402	0.8283	-
12.0748	210500	0.0343	0.2436	0.8267	-
12.1035	211000	0.0318	0.2374	0.8290	-
12.1322	211500	0.0341	0.2390	0.8279	-
12.1608	212000	0.0322	0.2416	0.8269	-
12.1895	212500	0.0343	0.2434	0.8265	-
12.2182	213000	0.0307	0.2438	0.8287	-
12.2469	213500	0.0297	0.2404	0.8252	-
12.2756	214000	0.0328	0.2400	0.8250	-
12.3043	214500	0.0321	0.2416	0.8249	-
12.3329	215000	0.0316	0.2420	0.8271	-
12.3616	215500	0.0364	0.2455	0.8282	-
12.3903	216000	0.032	0.2457	0.8269	-
12.4190	216500	0.0338	0.2438	0.8303	-
12.4477	217000	0.0348	0.2418	0.8293	-
12.4763	217500	0.034	0.2396	0.8287	-
12.5050	218000	0.0348	0.2439	0.8245	-
12.5337	218500	0.0324	0.2434	0.8236	-
12.5624	219000	0.0327	0.2451	0.8239	-
12.5911	219500	0.0312	0.2402	0.8262	-
12.6197	220000	0.034	0.2404	0.8237	-
12.6484	220500	0.0318	0.2483	0.8219	-
12.6771	221000	0.0357	0.2432	0.8248	-
12.7058	221500	0.0324	0.2476	0.8241	-
12.7345	222000	0.0335	0.2434	0.8264	-
12.7632	222500	0.0308	0.2493	0.8225	-
12.7918	223000	0.0328	0.2471	0.8236	-
12.8205	223500	0.0307	0.2402	0.8257	-
12.8492	224000	0.032	0.2432	0.8292	-
12.8779	224500	0.0312	0.2426	0.8270	-
12.9066	225000	0.0335	0.2412	0.8282	-
12.9352	225500	0.0333	0.2433	0.8265	-
12.9639	226000	0.0295	0.2426	0.8274	-
12.9926	226500	0.0343	0.2402	0.8263	-
13.0213	227000	0.0317	0.2410	0.8262	-
13.0500	227500	0.0283	0.2400	0.8276	-
13.0786	228000	0.0291	0.2460	0.8266	-
13.1073	228500	0.0328	0.2456	0.8258	-
13.1360	229000	0.0287	0.2451	0.8269	-
13.1647	229500	0.0327	0.2468	0.8275	-
13.1934	230000	0.0322	0.2427	0.8265	-
13.2221	230500	0.0311	0.2430	0.8256	-
13.2507	231000	0.0302	0.2425	0.8248	-
13.2794	231500	0.0288	0.2383	0.8265	-
13.3081	232000	0.0299	0.2397	0.8286	-
13.3368	232500	0.0332	0.2434	0.8259	-
13.3655	233000	0.0272	0.2449	0.8271	-
13.3941	233500	0.0327	0.2432	0.8260	-
13.4228	234000	0.0274	0.2450	0.8241	-
13.4515	234500	0.0303	0.2451	0.8243	-
13.4802	235000	0.028	0.2447	0.8252	-
13.5089	235500	0.0287	0.2443	0.8254	-
13.5375	236000	0.0279	0.2438	0.8242	-
13.5662	236500	0.0309	0.2443	0.8242	-
13.5949	237000	0.03	0.2426	0.8237	-
13.6236	237500	0.03	0.2441	0.8241	-
13.6523	238000	0.0309	0.2428	0.8233	-
13.6809	238500	0.0295	0.2417	0.8236	-
13.7096	239000	0.0297	0.2477	0.8240	-
13.7383	239500	0.0287	0.2463	0.8245	-
13.7670	240000	0.0306	0.2433	0.8243	-
13.7957	240500	0.03	0.2460	0.8229	-
13.8244	241000	0.0274	0.2445	0.8231	-
13.8530	241500	0.028	0.2430	0.8231	-
13.8817	242000	0.0284	0.2442	0.8224	-
13.9104	242500	0.0284	0.2438	0.8232	-
13.9391	243000	0.0304	0.2416	0.8239	-
13.9678	243500	0.0282	0.2436	0.8239	-
13.9964	244000	0.0286	0.2436	0.8232	-
14.0251	244500	0.0261	0.2442	0.8224	-
14.0538	245000	0.0282	0.2441	0.8236	-
14.0825	245500	0.0259	0.2444	0.8229	-
14.1112	246000	0.0274	0.2451	0.8222	-
14.1398	246500	0.0276	0.2447	0.8222	-
14.1685	247000	0.0239	0.2462	0.8220	-
14.1972	247500	0.0284	0.2436	0.8215	-
14.2259	248000	0.0306	0.2459	0.8218	-
14.2546	248500	0.0272	0.2428	0.8218	-
14.2833	249000	0.0282	0.2437	0.8213	-
14.3119	249500	0.0318	0.2439	0.8211	-
14.3406	250000	0.025	0.2457	0.8213	-
14.3693	250500	0.027	0.2435	0.8218	-
14.3980	251000	0.0276	0.2463	0.8215	-
14.4267	251500	0.029	0.2455	0.8215	-
14.4553	252000	0.0277	0.2440	0.8226	-
14.4840	252500	0.0248	0.2464	0.8224	-
14.5127	253000	0.0219	0.2452	0.8226	-
14.5414	253500	0.0264	0.2445	0.8237	-
14.5701	254000	0.026	0.2452	0.8231	-
14.5987	254500	0.0242	0.2454	0.8235	-
14.6274	255000	0.027	0.2469	0.8232	-
14.6561	255500	0.0265	0.2465	0.8229	-
14.6848	256000	0.0278	0.2463	0.8226	-
14.7135	256500	0.0286	0.2458	0.8229	-
14.7422	257000	0.0276	0.2460	0.8228	-
14.7708	257500	0.029	0.2463	0.8229	-
14.7995	258000	0.0255	0.2460	0.8226	-
14.8282	258500	0.0224	0.2462	0.8226	-
14.8569	259000	0.0251	0.2457	0.8227	-
14.8856	259500	0.0268	0.2465	0.8227	-
14.9142	260000	0.0271	0.2463	0.8227	-
14.9429	260500	0.029	0.2462	0.8227	-
14.9716	261000	0.0287	0.2464	0.8227	-
-1	-1	-	-	-	0.8024

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: 7 tokens mean: 10.38 tokens max: 45 tokens	min: 6 tokens mean: 12.8 tokens max: 39 tokens	min: 6 tokens mean: 13.4 tokens max: 50 tokens

sobamchan/roberta-base-no-mrl

SentenceTransformer based on FacebookAI/roberta-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/
roberta-base-no-mrl