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SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': '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("gabrielegabellone/all-mini-itprojectdocs-10000-6-epoch")
5# Run inference
6queries = [
7 "How is configuration managed in CoreOps, and what is the order of precedence?",
8]
9documents = [
10 '## Configuration\n\nCoreOps is highly configurable, allowing you to tailor its behavior to your specific environment. Configuration can be managed through a central `coreops.yaml` file, environment variables, or command-line flags. The order of precedence is: command-line flags > environment variables > `coreops.yaml` > default values.',
11 "## About PylonGrid\n\nPylonGrid aims to simplify the complexity of managing large-scale distributed computing environments. It acts as a central nervous system for your compute resources, abstracting away the underlying infrastructure details and providing a unified interface for submitting, monitoring, and managing computational jobs. Whether you're running scientific simulations, data processing pipelines, or microservices, PylonGrid ensures your workloads are executed on the most suitable resources, maximizing throughput and minimizing operational overhead.\n\nKey principles guiding PylonGrid's development:\n* **Efficiency:** Optimize resource utilization through intelligent scheduling algorithms.\n* **Reliability:** Ensure job completion even in the face of node failures or network partitions.\n* **Flexibility:** Support diverse workloads and integrate with various infrastructure types (on-prem, cloud, hybrid).\n* **Scalability:** Designed to manage thousands of nodes and millions of jobs.\n* **Usability:** Provide intuitive CLI and API for developers and operators.",
12 '## Getting Started: Configuration\n\nOmniTrail\'s configuration is primarily managed through environment variables for Docker Compose and values files for Helm deployments. Key configuration aspects include data sources, storage, and authentication.\n\n### Environment Variables (Docker Compose)\n\nEdit the `deploy/docker-compose.yml` file or create a `.env` file in the root directory. Common variables:\n\n* `OT_STORAGE_RETENTION_DAYS`: Number of days to retain data (e.g., `30`). Default is `7`.\n* `OT_API_PORT`: Port for the API service (e.g., `8081`). Default is `8081`.\n* `OT_WEB_PORT`: Port for the Web UI service (e.g., `8080`). Default is `8080`.\n* `OT_AUTH_ENABLED`: Set to `true` to enable user authentication. Default is `false`.\n* `OT_AUTH_JWT_SECRET`: Secret key for JWT token generation (required if `OT_AUTH_ENABLED=true`).\n\nExample `.env`:\n```dotenv\nOT_STORAGE_RETENTION_DAYS=90\nOT_AUTH_ENABLED=true\nOT_AUTH_JWT_SECRET=your_super_secret_jwt_key_here\n```\n\n### Helm Values (Kubernetes)\n\nFor Kubernetes, configuration is managed via the `values.yaml` file or by passing `--set` flags during `helm install` or `helm upgrade`.\n\nCreate a custom `my-values.yaml` file:\n```yaml\nomnitrail-core:\n storage:\n retentionDays: 90\nomnitrail-web:\n service:\n type: LoadBalancer\nomnitrail-api:\n auth:\n enabled: true\n jwtSecret: "your_super_secret_jwt_key_here"\n```\n\nThen install with:\n```bash\nhelm install omnitrail omnitrail/omnitrail -n omnitrail -f my-values.yaml\n```\n\n### Data Source Configuration\n\nOmniTrail agents and SDKs are configured separately to point to the OmniTrail ingestion endpoint. For example, an `omnitrail-agent` configuration might look like:\n\n```yaml\n# /etc/omnitrail-agent/config.yaml\ningestion_url: "http://omnitrail-api.omnitrail.svc.cluster.local:8081/api/v1/ingest"\nmetrics:\n enabled: true\n collect_interval: 15s\nlogs:\n enabled: true\n paths:\n - /var/log/*.log\n```\nRefer to the [Configuration Guide](https://docs.omnitrail.io/configuration) for a complete list of configurable parameters.',
13]
14query_embeddings = model.encode_query(queries)
15document_embeddings = model.encode_document(documents)
16print(query_embeddings.shape, document_embeddings.shape)
17# [1, 384] [3, 384]
18
19# Get the similarity scores for the embeddings
20similarities = model.similarity(query_embeddings, document_embeddings)
21print(similarities)
22# tensor([[ 0.8661, -0.0843, 0.1284]])itprojectdocs-devTripletEvaluator| Metric | Value |
|---|---|
| cosine_accuracy | 0.995 |
question and answer| question | answer | |
|---|---|---|
| type | string | string |
| details |
|
|
| question | answer |
|---|---|
What database is recommended for the AetherMap project? | ### Prerequisites[object Object]To run AetherMap, you will need:[object Object]* [object Object] Linux (Ubuntu 20.04+, CentOS 7+, RHEL 8+), macOS. Windows is supported via WSL2.[object Object]* [object Object] Recommended for containerized deployment (version 20.10+ and 1.29+ respectively).[object Object]* [object Object] Version 1.20+ (if building from source).[object Object]* [object Object] Version 18+ (if developing the web UI or building from source).[object Object]* [object Object] PostgreSQL 12+ (for persistent storage of configuration, historical data, and discovered topology). A local SQLite database can be used for quick testing.[object Object]* [object Object] 4 vCPUs, 8GB RAM, 50GB SSD storage (for small deployments). Larger deployments will require more resources. |
What container runtime is necessary for the CodeRun project to function? | ## Installation[object Object][object Object]CodeRun is designed for deployment in Linux environments, leveraging containerization for its core functionality. It can be deployed as a standalone service or integrated into existing infrastructure. This section outlines the various methods to get CodeRun up and running.[object Object][object Object]Regardless of the installation method, CodeRun requires a compatible container runtime (like Docker) to be installed and properly configured on the host system. It's recommended to run CodeRun on a dedicated server or VM for optimal performance and security isolation. |
How do I configure CoreForge for a new project using the [object Object] file? | ## Configuration[object Object][object Object]CoreForge uses a [object Object] file (or [object Object]) in your project directory for global settings and provider configurations. Provider-specific credentials are typically managed via environment variables or cloud provider CLI configurations.[object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object]* [object Object]: A unique identifier for your infrastructure project.[object Object]* [object Object]: Defines where CoreForge stores its state file. Options include [object Object] (for developme... |
MultipleNegativesRankingLoss with these parameters:
1{
2 "scale": 20.0,
3 "similarity_fct": "cos_sim",
4 "gather_across_devices": false
5}question and answer| question | answer | |
|---|---|---|
| type | string | string |
| details |
|
|
| question | answer |
|---|---|
How can I benchmark the performance of optimized code against original code using the LoopNest project? | ## Benchmarking & Performance Analysis[object Object][object Object]LoopNest includes built-in capabilities to benchmark the performance of original versus optimized code, providing empirical evidence of its effectiveness. It also generates detailed reports to help users understand the performance impact of transformations.[object Object][object Object]### Running Benchmarks[object Object][object Object]To run benchmarks, you typically need to provide a test harness or a main function that calls the kernel you wish to benchmark. LoopNest can then compile and execute both versions.[object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object]* [object Object]: The command to run performance comparisons.[object Object]* [object Object]: Number of times to run the kernel for measurement.[object Object]* [object Object]: Number of initial runs to warm up caches and stabilize CPU frequencies.[object Object]* [object Object]: Generates a JSON report containing detailed timin... |
What kind of cloud provider credentials and database are needed before installing NestHawk? | ### Prerequisites[object Object][object Object]Before installing NestHawk, ensure you have the following:[object Object][object Object]* [object Object] Required if building from source.[object Object]* [object Object] Required if using Docker.[object Object]* [object Object] Appropriate IAM roles or service accounts with read-only access to your cloud resources for discovery. Refer to our [object Object] for detailed instructions on configuring permissions.[object Object]* [object Object] A database instance for storing NestHawk's operational data and analysis results. |
How can a developer set the default cloud provider for LambdaShell using the [object Object] command? | ## Configuration[object Object][object Object]LambdaShell uses a global configuration file located at [object Object] (or [object Object] on Windows) to store default settings and cloud provider credentials. This file is automatically created upon your first use of [object Object] or [object Object].[object Object][object Object]### Key Configuration Options[object Object][object Object]* [object Object] Configure profiles for AWS, Azure, and GCP. LambdaShell leverages the credentials configured in your respective cloud provider CLIs (e.g., AWS profiles, Azure service principals, GCP project IDs).[object Object]* [object Object] Set a default region or location for your deployments to avoid specifying it with every command.[object Object]* [object Object] Define default log retention periods for functions.[object Object]* [object Object] Set alerts or warnings for estimated function costs.[object Object][object Object]### Example [object Object][object Object][object Object]```json[object Object]{[object Object] "defaultProvider": "aws",[object Object] "aws": {[object Object] "defaultRegion": "us-east-1",[object Object] "profile": "default"[object Object] },[object Object]... |
MultipleNegativesRankingLoss with these parameters:
1{
2 "scale": 20.0,
3 "similarity_fct": "cos_sim",
4 "gather_across_devices": false
5}eval_strategy: epochper_device_train_batch_size: 256per_device_eval_batch_size: 256num_train_epochs: 6warmup_ratio: 0.1fp16: Truebatch_sampler: no_duplicatesoverwrite_output_dir: Falsedo_predict: Falseeval_strategy: epochprediction_loss_only: Trueper_device_train_batch_size: 256per_device_eval_batch_size: 256per_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: 1.0num_train_epochs: 6max_steps: -1lr_scheduler_type: linearlr_scheduler_kwargs: {}warmup_ratio: 0.1warmup_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_torch_fusedoptim_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: Falsehub_revision: Nonegradient_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: Falseliger_kernel_config: Noneeval_use_gather_object: Falseaverage_tokens_across_devices: Falseprompts: Nonebatch_sampler: no_duplicatesmulti_dataset_batch_sampler: proportionalrouter_mapping: {}learning_rate_mapping: {}| Epoch | Step | Training Loss | Validation Loss | itprojectdocs-dev_cosine_accuracy |
|---|---|---|---|---|
| -1 | -1 | - | - | 0.9339 |
| 1.0 | 32 | 0.3728 | 0.1695 | 0.9888 |
| 2.0 | 64 | 0.1313 | 0.1083 | 0.9944 |
| 3.0 | 96 | 0.0916 | 0.0970 | 0.9942 |
| 4.0 | 128 | 0.0681 | 0.0892 | 0.9944 |
| 5.0 | 160 | 0.0632 | 0.0864 | 0.9950 |
| 6.0 | 192 | 0.0585 | 0.0843 | 0.9950 |
| -1 | -1 | - | - | 0.9950 |
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{henderson2017efficient,
2 title={Efficient Natural Language Response Suggestion for Smart Reply},
3 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},
4 year={2017},
5 eprint={1705.00652},
6 archivePrefix={arXiv},
7 primaryClass={cs.CL}
8}