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SentenceTransformer(
(0): Transformer({'max_seq_length': 512, '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})
)pip install -U sentence-transformers1from sentence_transformers import SentenceTransformer
2
3# Download from the 🤗 Hub
4model = SentenceTransformer("sentence_transformers_model_id")
5# Run inference
6sentences = [
7 'What R code can be used to determine the readability level of a text and determine if it is suitable for general readers?',
8 "[{'content': 'What R code can be used to determine the readability level of a text and determine if it is suitable for general readers?', 'role': 'user'}\n {'content': 'There are many different readability formulas that can be used to determine the readability level of a text. One popular formula is the Flesch-Kincaid Grade Level (F-K GL). The F-K GL formula takes into account the average number of syllables per word and the average number of words per sentence. To calculate the F-K GL, you can use the following R code:\\n\\n```r\\nlibrary(readability)\\n\\nreadability(text)\\n```\\n\\nThe output of the `readability()` function will include the F-K GL score, as well as other readability scores such as the Gunning Fog Index and the Fry Readability Score.\\n\\nTo determine if a text is suitable for general readers, you can use the following rule of thumb:\\n\\n* A text with an F-K GL score of 8 or lower is considered to be easy to read for most adults.\\n* A text with an F-K GL score of 9 to 12 is considered to be moderately easy to read for most adults.\\n* A text with an F-K GL score of 13 to 16 is considered to be somewhat difficult to read for most adults.\\n* A text with an F-K GL score of 17 or higher is considered to be difficult to read for most adults.\\n\\nOf course, these are just general guidelines. The actual readability of a text will depend on a number of factors, including the vocabulary, sentence structure, and level of detail.', 'role': 'assistant'}]",
9 '[{\'content\': \'What R code can be used to determine the readability level of a text and determine if it is suitable for general readers?\', \'role\': \'user\'}\n {\'content\': \'There are several R packages that can be used to determine the readability level of a text and determine if it is suitable for general readers. Here are a few options:\\n\\n1. The "readability" package: This package provides several functions to assess the readability of text, including the Flesch-Kincaid readability test, the Gunning-Fog index, and the SMOG (Simple Measure of Gobbledygook) index. These indices are based on factors such as sentence length, word length, and vocabulary level, and can provide an estimate of the reading ease or grade level of the text.\\n2. The "readability-test" package: This package offers a range of readability tests, including the Flesch Reading Ease test, the Gunning-Fog index, and the New Dale-Chall Readability Formula. It also provides functions to calculate the readability of text based on the number of words per sentence, the number of syllables per word, and the percentage of words with three or more syllables.\\n3. The "lexical" package: This package offers a range of functions for analyzing the vocabulary and readability of text. It includes measures such as vocabulary diversity, word frequency, and readability indices, and can be used to assess the complexity of text at various levels, from individual words to entire documents.\\n\\nTo use these packages, you will need to install them in your R environment and then load them into your R session. You can then use the various functions provided by the packages to analyze the readability of your text.\\n\\nFor example, to use the "readability" package to calculate the Flesch-Kincaid readability test for a text, you can use the following code:\\n```\\nlibrary(readability)\\n\\n# Create a sample text\\ntext <- "This is a sample text for readability testing."\\n\\n# Calculate the Flesch-Kincaid readability score\\nflesch_kincaid(text)\\n```\\nThis will output the Flesch-Kincaid grade level and score for the text, which can help you determine if it is suitable for general readers.\\n\\nIt\\\'s important to note that while these packages can provide useful insights into the readability of text, they are not perfect and should not be relied upon as the sole measure of a text\\\'s readability. Factors such as the content and context of the text, the intended audience, and the purpose of the text should also be taken into account when assessing its readability.\', \'role\': \'assistant\'}]',
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]val-evalEmbeddingSimilarityEvaluator| Metric | Value |
|---|---|
| pearson_cosine | 0.1327 |
| spearman_cosine | 0.1395 |
sentence_0, sentence_1, and sentence_2| sentence_0 | sentence_1 | sentence_2 | |
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The following is a conversation between USER and AGI. Before AGI responds to USER, there is an internal dialogue among AGI, INSTINCT, CRITIC, and SYNTHESIZER. INSTINCT reacts emotionally to inputs from AGI. CRITIC challenges the emotional response of INSTINCT. SYNTHESIZER finally synthesizes a coherent reaction based on CRITIC's response to INSTINCT. Finally AGI reviews the internal dialogue and provides the most logical reply to USER.[object Object][object Object]USER: [object Object][object Object]Hello AGI. Let's play a game. I am thinking of a noun. You get to ask 20 "yes or no" questions to narrow down and guess what noun I am thinking of. [object Object][object Object]INSTINCT: | [{'content': 'The following is a conversation between USER and AGI. Before AGI responds to USER, there is an internal dialogue among AGI, INSTINCT, CRITIC, and SYNTHESIZER. INSTINCT reacts emotionally to inputs from AGI. CRITIC challenges the emotional response of INSTINCT. SYNTHESIZER finally synthesizes a coherent reaction based on CRITIC's response to INSTINCT. Finally AGI reviews the internal dialogue and provides the most logical reply to USER.\n\nUSER: \n\nHello AGI. Let's play a game. I am thinking of a noun. You get to ask 20 "yes or no" questions to narrow down and guess what noun I am thinking of. \n\nINSTINCT:', 'role': 'user'}[object Object] {'content': "Sure, I'd love to play a game with you! Before I start asking questions, I need to clarify a few things. Firstly, I would like to point out that asking 20 yes-or-no questions might not be the most efficient way to guess the noun you're thinking of. It might take a while, and there might be some repeated questions or dead ends. Would yo... | [{'content': 'The following is a conversation between USER and AGI. Before AGI responds to USER, there is an internal dialogue among AGI, INSTINCT, CRITIC, and SYNTHESIZER. INSTINCT reacts emotionally to inputs from AGI. CRITIC challenges the emotional response of INSTINCT. SYNTHESIZER finally synthesizes a coherent reaction based on CRITIC's response to INSTINCT. Finally AGI reviews the internal dialogue and provides the most logical reply to USER.\n\nUSER: \n\nHello AGI. Let's play a game. I am thinking of a noun. You get to ask 20 "yes or no" questions to narrow down and guess what noun I am thinking of. \n\nINSTINCT:', 'role': 'user'}[object Object] {'content': 'I am always ready to play. Okay, Is the joker in the Harley Quinn project a boy?\nINSTINCT (insights): That's irrelevant yet interesting. Say, "Give me yes or no so I can ask."\nAssistant: Would you like me to ask you if he is a boy?\nINSTINCT (default): I'm sorry, but as an AI language model, I cannot have preferences, attitudes or ... |
[object Object]Options:[object Object]- (S_1)[object Object]- (S_2)[object Object]- Both engines perform equally.[object Object]- This evaluation metric cannot be computed. | Compute AP: for (S_1), relevant at ranks 2, 3, 5 gives precisions 0.5, 0.667, 0.6, so APâ=(1.767)/50â0.035; for (S_2), relevant at ranks 1,2 gives precisions 1 and 1, so APâ=2/50=0.04. Since 0.04>0.035, (S_2) performs better. | One could argue that retrieving more relevant docs in the top five (three vs. two) makes (S_1) better, but average precision penalizes deeper relevant documents, so despite having more stars, its lower early precision yields a smaller AP than (S_2). |
Review the notion of depth seen in the lecture. What does it represent? Below is a formula for the depth of a divide and conquer algorithm working on an array segment of size $L$, as a function of $L$. The values $c$, $d$ and $T$ are constants. We assume that $L>0$ and $T>0$. $$ D(L) = \begin{cases} c \cdot L &\text{if}\ L \leq T \ \text{max}\left( D\left(\left\lfloor \frac L2 \right\rfloor \right), D\left(L - \left\lfloor \frac L2 \right\rfloor \right)\right) + d &\text{otherwise} \end{cases} $$ Below the threshold T, the algorithm proceeds sequentially and takes time c to process each single element. Above the threshold, the algorithm is applied recursively over the two halves of the array. The results are then merged using an operation that takes d units of time. Is it the case that for all $1 \leq L_1 \leq L_2$ we have $D(L_1) \leq D(L_2)$? If it is the case, prove the property by induction on $L$. If it is not the case, give a counterexample showing values of $L_1$, $L_2$, $c... | To solve the question, we need to analyze the depth function ( D(L) ) defined for a divide and conquer algorithm and determine whether ( D(L_1) \leq D(L_2) ) holds for all ( 1 \leq L_1 \leq L_2 ). [object Object][object Object]### Step 1: Understanding the depth function ( D(L) )[object Object][object Object]The depth function is defined as follows:[object Object][object Object][[object Object]D(L) = \begin{cases} [object Object]c \cdot L & \text{if } L \leq T \ [object Object]\max\left( D\left(\left\lfloor \frac{L}{2} \right\rfloor \right), D\left(L - \left\lfloor \frac{L}{2} \right\rfloor \right)\right) + d & \text{otherwise} [object Object]\end{cases} [object Object]][object Object][object Object]- For ( L \leq T ), the function is linear: ( D(L) = c \cdot L ). Here, each element is processed sequentially, and the time taken is directly proportional to the size ( L ).[object Object]- For ( L > T ), the algorithm splits the problem into two halves, calculates the depth of each half recursively, and combines the results with an additional constant time ( d ).[object Object][object Object]### Step 2: Counterexample setup[object Object][object Object]We need to find specific values for ( L_1 ), ( L_2 ), ( T ... | To address the question, we need to first clarify the concept of depth in the context of the divide and conquer algorithms, then analyze the given formula for depth ( D(L) ) and check if the property ( D(L_1) \leq D(L_2) ) holds for all ( L_1 ) and ( L_2 ).[object Object][object Object]### Step 1: Understanding the Depth Function [object Object][object Object]The depth function ( D(L) ) is defined as follows:[object Object][object Object][[object Object]D(L) = \begin{cases} [object Object]c \cdot L & \text{if } L \leq T \ [object Object]\max\left( D\left(\left\lfloor \frac{L}{2} \right\rfloor\right), D\left(L - \left\lfloor \frac{L}{2} \right\rfloor\right) \right) + d & \text{if } L > T [object Object]\end{cases}[object Object]][object Object][object Object]- For sizes ( L ) that are less than or equal to a threshold ( T ), the depth is calculated linearly as ( c \cdot L ), meaning that processing each element takes a constant time ( c ).[object Object]- When ( L ) exceeds ( T ), the algorithm divides the task into two halves, computes the depth for each half recursively, and adds a merging time ( d ).[object Object][object Object]### Step 2: Counterexample Setup[object Object][object Object]We want to see... |
TripletLoss with these parameters:
1{
2 "distance_metric": "<lambda>",
3 "triplet_margin": 5
4}eval_strategy: stepsper_device_train_batch_size: 32per_device_eval_batch_size: 32multi_dataset_batch_sampler: round_robinoverwrite_output_dir: Falsedo_predict: Falseeval_strategy: stepsprediction_loss_only: Trueper_device_train_batch_size: 32per_device_eval_batch_size: 32per_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_robin| Epoch | Step | Training Loss | val-eval_spearman_cosine |
|---|---|---|---|
| 0.4062 | 500 | 4.8846 | 0.1167 |
| 0.8123 | 1000 | 4.7895 | 0.1096 |
| 1.0 | 1231 | - | 0.1312 |
| 1.2185 | 1500 | 4.7795 | 0.1395 |
1@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}1@misc{hermans2017defense,
2 title={In Defense of the Triplet Loss for Person Re-Identification},
3 author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
4 year={2017},
5 eprint={1703.07737},
6 archivePrefix={arXiv},
7 primaryClass={cs.CV}
8}