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SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(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})
)pip install -U sentence-transformers1from sentence_transformers import SentenceTransformer
2
3# Download from the 🤗 Hub
4model = SentenceTransformer("ML5562/fine-tuned-scibert_scivocab_uncased-all-json-M1_testing")
5# Run inference
6sentences = [
7 'You are working on an app which is a search engine for cat photos. The app works by making requests to a server which stores the photos. Users search for cat photos and see a batch of results at a time; they can tap on a photo to see it full screen. You are getting two main complaints from users about the app’s performance:\n1. When going from a page of results to the next one, the photos take too long to load\n2. When going back to the search results after looking at a picture, the photos take too long to be re-downloaded\nFor each of these complaints, write exactly one sentence giving a possible solution and explaining why it helps:',
8 "### Addressing User Complaints in Cat Photo Search App\n\n#### Complaint 1: Slow Loading Time When Transitioning Between Pages\n\n**Solution:** Implement a combination of lazy loading and a client-side caching strategy using a service worker to store previously fetched images in the browser's Cache Storage API. \n\n**Explanation:** Lazy loading allows images to be loaded only as they come into the viewport, reducing initial load times. When users navigate to a new page of results, the service worker can quickly serve images that have already been cached, minimizing the need for round trips to the server. For example, if a user views the first page and then navigates to the second page, any images from the first page that have been cached will load almost instantaneously. \n\n**Counterarguments:** While some may argue that caching can lead to stale data, implementing a cache invalidation strategy (e.g., using versioning or timestamps) can ensure that users always receive the most recent image uploads without significantly compromising load times.\n\n---\n\n#### Complaint 2: Slow Reloading of Photos When Returning to Search Results\n\n**Solution:** Implement an in-memory cache using a data structure like a Least Recently Used (LRU) cache to store images that users have recently viewed, along with their metadata to facilitate quick retrieval. \n\n**Explanation:** By maintaining an LRU cache, the app can keep a limited number of images in memory, allowing for rapid access when users return to the search results. For instance, if a user views a photo and then navigates back, the app can fetch the image from memory rather than re-fetching it from the server, significantly improving the perceived performance. \n\n**Counterarguments:** Critics might suggest that maintaining an in-memory cache could consume excessive memory, especially on devices with limited resources. However, by implementing a size limit on the cache and evicting the least recently accessed items, we can strike a balance between performance and memory usage, ensuring that the cache remains efficient without overwhelming device resources.\n\n### Conclusion\nBy utilizing a combination of lazy loading with service worker caching for page transitions and an LRU cache for recently viewed images, we can effectively address user complaints regarding slow loading times, enhancing the overall user experience in the cat photo search app while also considering potential limitations and counterarguments.",
9 "1. To address the slow loading of photos when navigating between pages of results, implement lazy loading, which only loads images as they come into the user's view, reducing the initial load time and improving the overall user experience by minimizing the amount of data transferred at once.\n\n2. To speed up the re-downloading of photos after returning from a full-screen view, consider caching previously viewed images on the user's device so they can be accessed quickly without needing to be re-downloaded, thus enhancing the responsiveness of the app and reducing data usage.",
10]
11embeddings = model.encode(sentences)
12print(embeddings.shape)
13# [3, 768]
14
15# Get the similarity scores for the embeddings
16similarities = model.similarity(embeddings, embeddings)
17print(similarities.shape)
18# [3, 3]val-evalTripletEvaluator| Metric | Value |
|---|---|
| cosine_accuracy | 0.6155 |
sentence_0, sentence_1, and sentence_2| sentence_0 | sentence_1 | sentence_2 | |
|---|---|---|---|
| type | string | string | string |
| details |
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| sentence_0 | sentence_1 | sentence_2 |
|---|---|---|
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. | In a reservation station, the element that should be selected is typically the one with the highest priority based on its readiness and the order of instructions. Choosing the earliest ready instruction can help minimize pipeline stalls and improve overall throughput, allowing subsequent instructions to proceed without unnecessary delays. | The selection should favor the element that can execute first based on data dependencies and resource availability. If multiple elements are ready, selecting one with fewer dependencies can enhance execution efficiency and reduce latency, as it allows the out-of-order execution mechanism to utilize available execution units effectively. |
The data contains information about submissions to a prestigious machine learning conference called ICLR. Columns:[object Object]year, paper, authors, ratings, decisions, institution, csranking, categories, authors_citations, authors_publications, authors_hindex, arxiv. The data is stored in a pandas.DataFrame format. [object Object][object Object]Create 3 new fields in the dataframe corresponding to the median value of the number of citations per author, the number of publications per author, and the h-index per author. So for instance, for the row authors_publications, you will create an additional column, e.g. authors_publications_median, containing the median number of publications per author in each paper. | Let's think step by step.[object Object][object Object]1. [object Object]: We have a DataFrame that contains several columns related to papers submitted to the ICLR conference, including columns for citations, publications, and h-index of the authors. Each of these columns contains data in a specific format (likely strings representing lists of values separated by semicolons).[object Object][object Object]2. [object Object]: We need to create three new fields in the DataFrame:[object Object] - [object Object]: The median number of citations per author for each paper.[object Object] - [object Object]: The median number of publications per author for each paper.[object Object] - [object Object]: The median h-index per author for each paper.[object Object][object Object]3. [object Object]: Before calculating the median, we should ensure that any invalid values (like '-1', which might indicate missing data) are removed from the lists of values in the respective columns. This will allow us to calculate the median accurately.[object Object][object Object]4. **Processing Eac... | To create the new fields in the DataFrame, we will follow these steps:[object Object][object Object]1. [object Object]: We will need to split the string data in the columns [object Object], [object Object], and [object Object] to work with the individual authors' data.[object Object][object Object]2. [object Object]: For each paper, we will compute the median of the values for citations per author, publications per author, and h-index per author.[object Object][object Object]3. [object Object]: We will then assign these median values to new columns in the DataFrame.[object Object][object Object]Let's outline the reasoning and the answer based on these steps:[object Object][object Object]- [object Object]: We will use the [object Object] function on the [object Object], [object Object], and [object Object] columns to convert the string representation of each author's metrics into list form.[object Object][object Object]- [object Object]: Using the [object Object] function, we will compute the median for each paper's authors.[object Object][object Object]- [object Object]: Finally, we will assign the computed medians to new columns named `au... |
What is WRONG regarding the Transformer model? | ### Correct Answer(s): [object Object]1. [object Object][object Object][object Object]### Reasoning:[object Object][object Object]1. [object Object]: The Transformer model indeed uses a self-attention mechanism to compute representations of the input and output. This statement is true regarding the functionality of the Transformer.[object Object][object Object]2. [object Object]: The statement "Its computation cannot be parallelized compared to LSTMs and other sequential models" is incorrect. One of the main advantages of the Transformer architecture over LSTMs and other recurrent models is that it allows for parallelization during training. Since Transformers process all tokens in the input sequence simultaneously rather than sequentially, they can leverage parallel computation effectively, leading to faster training times.[object Object][object Object]3. [object Object]: The statement "Its complexity is quadratic to the input size" is true. The self-attention mechanism in Transformers has a time and space complexity of... | The statements regarding the Transformer model that are incorrect are:[object Object][object Object]1. [object Object][object Object][object Object]### Reasoning:[object Object][object Object]1. [object Object]: One of the most significant advantages of the Transformer model over LSTMs (Long Short-Term Memory networks) and other sequential models is its ability to parallelize computations. In LSTMs, computations are inherently sequential because each time step depends on the previous one, making it difficult to leverage parallel computing effectively. In contrast, Transformers use the self-attention mechanism, where all tokens in the input can be processed simultaneously. This leads to significantly faster training times and allows for the use of more efficient hardware, such as GPUs.[object Object][object Object]2. [object Object]: The statement "Its complexity is quadratic to the input size" is actually correct. The self-attention mechanism of Transformers computes attention scores for each pair of tokens,... |
TripletLoss with these parameters:
1{
2 "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
3 "triplet_margin": 5
4}eval_strategy: stepsper_device_train_batch_size: 4per_device_eval_batch_size: 4num_train_epochs: 1fp16: Truemulti_dataset_batch_sampler: round_robinoverwrite_output_dir: Falsedo_predict: Falseeval_strategy: stepsprediction_loss_only: Trueper_device_train_batch_size: 4per_device_eval_batch_size: 4per_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: 1max_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: Truefp16_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}fsdp_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: Nonedispatch_batches: Nonesplit_batches: 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_cosine_accuracy |
|---|---|---|---|
| 0.1031 | 500 | 4.7429 | 0.5101 |
| 0.2063 | 1000 | 4.6472 | 0.5703 |
| 0.3094 | 1500 | 4.388 | 0.5833 |
| 0.4125 | 2000 | 4.3492 | 0.5930 |
| 0.5157 | 2500 | 4.4078 | 0.6000 |
| 0.6188 | 3000 | 4.3554 | 0.6023 |
| 0.7219 | 3500 | 4.3187 | 0.6143 |
| 0.8251 | 4000 | 4.3159 | 0.6110 |
| 0.9282 | 4500 | 4.1508 | 0.6147 |
| 1.0 | 4848 | - | 0.6155 |
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}