SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(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})
(2): Normalize()
)pip install -U sentence-transformers1from sentence_transformers import SentenceTransformer
2
3# Download from the 🤗 Hub
4model = SentenceTransformer("AShi846/all-MiniLM-L6-v2_rag_ft_e-3")
5# Run inference
6sentences = [
7 'The data contains information about submissions to a prestigious machine learning conference called ICLR. Columns:\nyear, paper, authors, ratings, decisions, institution, csranking, categories, authors_citations, authors_publications, authors_hindex, arxiv. The data is stored in a pandas.DataFrame format. \n\nCreate two fields called has_top_company and has_top_institution. The field has_top_company equals 1 if the article contains an author in the following list of companies ["Facebook", "Google", "Microsoft", "Deepmind"], and 0 otherwise. The field has_top_institution equals 1 if the article contains an author in the top 10 institutions according to CSRankings.',
8 "Recall that, in the Hedge algorithm we learned in class, the total loss over time is upper bounded by $\\sum_{t = 1}^T m_i^t + \\frac{\\ln N}{\\epsilon} + \\epsilon T$. In the case of investments, we want to do almost as good as the best investment. Let $g_i^t$ be the fractional change of the value of $i$'th investment at time $t$. I.e., $g_i^t = (100 + change(i))/100$, and $p_i^{t+1} = p_i^{t} \\cdot g_i^t$. Thus, after time $T$, $p_i^{T+1} = p_i^1 \\prod_{t = 1}^T g_i^t$. To get an analogous bound to that of the Hedge algorithm, we take the logarithm. The logarithm of the total gain would be $\\sum_{t=1}^T \\ln g_i^t$. To convert this into a loss, we multiply this by $-1$, which gives a loss of $\\sum_{t=1}^T (- \\ln g_i^t)$. Hence, to do almost as good as the best investment, we make our cost vectors to be $m_i^t = - \\ln g_i^t$. Now, from the analysis of Hedge algorithm in the lecture, it follows that for all $i \\in [N]$, $$\\sum_{t = 1}^T p^{(t)}_i \\cdot m^{(t)} \\leq \\sum_{t = 1}^{T} m^{(t)}_i + \\frac{\\ln N}{\\epsilon} + \\epsilon T.$$ Taking the exponent in both sides, We have that \\begin{align*} \\exp \\left( \\sum_{t = 1}^T p^{(t)}_i \\cdot m^{(t)} \\right) &\\leq \\exp \\left( \\sum_{t = 1}^{T} m^{(t)}_i + \\frac{\\ln N}{\\epsilon} + \\epsilon T \\right)\\\\ \\prod_{t = 1}^T \\exp( p^{(t)}_i \\cdot m^{(t)} ) &\\leq \\exp( \\ln N / \\epsilon + \\epsilon T) \\prod_{t = 1}^T \\exp(m^t_i) \\\\ \\prod_{t = 1}^T \\prod_{i \\in [N]} (1 / g_i^t)^{p^{(t)}_i} &\\leq \\exp( \\ln N / \\epsilon + \\epsilon T) \\prod_{t = 1}^{T} (1/g^{(t)}_i) \\end{align*} Taking the $T$-th root on both sides, \\begin{align*} \\left(\\prod_{t = 1}^T \\prod_{i \\in [N]} (1 / g_i^t)^{p^{(t)}_i} \\right)^{(1/T)} &\\leq \\exp( \\ln N / \\epsilon T + \\epsilon ) \\left( \\prod_{t = 1}^{T} (1/g^{(t)}_i) \\right)^{(1/T)}. \\end{align*} This can be interpreted as the weighted geometric mean of the loss is not much worse than the loss of the best performing investment.",
9 '1',
10]
11embeddings = model.encode(sentences)
12print(embeddings.shape)
13# [3, 384]
14
15# Get the similarity scores for the embeddings
16similarities = model.similarity(embeddings, embeddings)
17print(similarities.shape)
18# [3, 3]sentence_0, sentence_1, and label| sentence_0 | sentence_1 | label | |
|---|---|---|---|
| type | string | string | float |
| details |
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| sentence_0 | sentence_1 | label |
|---|---|---|
Assume that your team is discussing the following java code:[object Object][object Object]public final class DataStructure {[object Object] public void add(int val) { /[object Object]/ }[object Object][object Object] private boolean isFull() { /[object Object]/ }[object Object]}[object Object][object Object]Your colleagues were changing the parameter type of "add" to an "Integer". Explain whether this breaks backward compatibility and why or why not (also without worrying about whether this is a good or a bad thing). | D(cat,dog)=2[object Object]D(cat,pen)=6 [object Object]D(cat,table)=6[object Object]D(dog,pen)=6 [object Object]D(dog,table)=6[object Object]D(pen,table)=2 | 0.1 |
If several elements are ready in a reservation station, which[object Object] one do you think should be selected? extbf{Very briefly} discuss[object Object] the options. | Obama SLOP/1 Election returns document 3 Obama SLOP/2 Election returns documents 3 and T Obama SLOP/5 Election returns documents 3,1, and 2 Thus the values are X=1, x=2, and x=5 Obama = (4 : {1 - [3}, {2 - [6]}, {3 [2,17}, {4 - [1]}) Election = (4: {1 - [4)}, (2 - [1, 21), {3 - [3]}, {5 - [16,22, 51]}) | 0.1 |
If process i fails, then eventually all processes j≠i fail[object Object]Is the following true? If no process j≠i fails, then process i has failed | No, it is almost certain that it would not work. On a[object Object] dynamically-scheduled processor, the user is not supposed to[object Object] see the returned value from a speculative load because it will[object Object] never be committed; the whole idea of the attack is to make[object Object] speculatively use of the result and leave a microarchitectural[object Object] trace of the value before the instruction is squashed. In[object Object] Itanium, the returned value of the speculative load[object Object] instruction is architecturally visible and checking whether[object Object] the load is valid is left to the compiler which, in fact,[object Object] might or might not perform such a check. In this context, it[object Object] would have been a major implementation mistake if the value[object Object] loaded speculatively under a memory access violation were the[object Object] true one that the current user is not allowed to access;[object Object] clearly, the implementa... | 0.1 |
CosineSimilarityLoss with these parameters:
1{
2 "loss_fct": "torch.nn.modules.loss.MSELoss"
3}per_device_train_batch_size: 16per_device_eval_batch_size: 16multi_dataset_batch_sampler: round_robinoverwrite_output_dir: Falsedo_predict: Falseeval_strategy: noprediction_loss_only: Trueper_device_train_batch_size: 16per_device_eval_batch_size: 16per_gpu_train_batch_size: Noneper_gpu_eval_batch_size: Nonegradient_accumulation_steps: 1eval_accumulation_steps: Nonetorch_empty_cache_steps: Nonelearning_rate: 5e-05weight_decay: 0.0adam_beta1: 0.9adam_beta2: 0.999adam_epsilon: 1e-08max_grad_norm: 1num_train_epochs: 3max_steps: -1lr_scheduler_type: linearlr_scheduler_kwargs: {}warmup_ratio: 0.0warmup_steps: 0log_level: passivelog_level_replica: warninglog_on_each_node: Truelogging_nan_inf_filter: Truesave_safetensors: Truesave_on_each_node: Falsesave_only_model: Falserestore_callback_states_from_checkpoint: Falseno_cuda: Falseuse_cpu: Falseuse_mps_device: Falseseed: 42data_seed: Nonejit_mode_eval: Falseuse_ipex: Falsebf16: Falsefp16: Falsefp16_opt_level: O1half_precision_backend: autobf16_full_eval: Falsefp16_full_eval: Falsetf32: Nonelocal_rank: 0ddp_backend: Nonetpu_num_cores: Nonetpu_metrics_debug: Falsedebug: []dataloader_drop_last: Falsedataloader_num_workers: 0dataloader_prefetch_factor: Nonepast_index: -1disable_tqdm: Falseremove_unused_columns: Truelabel_names: Noneload_best_model_at_end: Falseignore_data_skip: Falsefsdp: []fsdp_min_num_params: 0fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}tp_size: 0fsdp_transformer_layer_cls_to_wrap: Noneaccelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed: Nonelabel_smoothing_factor: 0.0optim: adamw_torchoptim_args: Noneadafactor: Falsegroup_by_length: Falselength_column_name: lengthddp_find_unused_parameters: Noneddp_bucket_cap_mb: Noneddp_broadcast_buffers: Falsedataloader_pin_memory: Truedataloader_persistent_workers: Falseskip_memory_metrics: Trueuse_legacy_prediction_loop: Falsepush_to_hub: Falseresume_from_checkpoint: Nonehub_model_id: Nonehub_strategy: every_savehub_private_repo: Nonehub_always_push: Falsegradient_checkpointing: Falsegradient_checkpointing_kwargs: Noneinclude_inputs_for_metrics: Falseinclude_for_metrics: []eval_do_concat_batches: Truefp16_backend: autopush_to_hub_model_id: Nonepush_to_hub_organization: Nonemp_parameters:auto_find_batch_size: Falsefull_determinism: Falsetorchdynamo: Noneray_scope: lastddp_timeout: 1800torch_compile: Falsetorch_compile_backend: Nonetorch_compile_mode: Noneinclude_tokens_per_second: Falseinclude_num_input_tokens_seen: Falseneftune_noise_alpha: Noneoptim_target_modules: Nonebatch_eval_metrics: Falseeval_on_start: Falseuse_liger_kernel: Falseeval_use_gather_object: Falseaverage_tokens_across_devices: Falseprompts: Nonebatch_sampler: batch_samplermulti_dataset_batch_sampler: round_robin1@inproceedings{reimers-2019-sentence-bert,
2 title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
3 author = "Reimers, Nils and Gurevych, Iryna",
4 booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
5 month = "11",
6 year = "2019",
7 publisher = "Association for Computational Linguistics",
8 url = "https://arxiv.org/abs/1908.10084",
9}