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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})
(2): Normalize()
)pip install -U sentence-transformers1from sentence_transformers import SentenceTransformer
2
3# Download from the 🤗 Hub
4model = SentenceTransformer("CharlesPing/finetuned-all-minilm-l12-v2-climate-v2")
5# Run inference
6sentences = [
7 'The Greenland ice sheet is at least 400,000 years old and warming was not global when Europeans settled in Greeland 1,000 years ago',
8 'Between 2001 and 2005: Sermeq Kujalleq broke up, losing 93 square kilometres (36\xa0sq\xa0mi) and raised awareness worldwide of glacial response to global climate change.',
9 'IPCC authors concluded ECS is very likely to be greater than 1.5\xa0°C (2.7\xa0°F) and likely to lie in the range 2 to 4.5\xa0°C (4 to 8.1\xa0°F), with a most likely value of about 3\xa0°C (5\xa0°F).',
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]climate-val-simplerInformationRetrievalEvaluator| Metric | Value |
|---|---|
| cosine_accuracy@1 | 0.5122 |
| cosine_accuracy@3 | 0.7805 |
| cosine_accuracy@5 | 0.8455 |
| cosine_accuracy@10 | 0.9024 |
| cosine_precision@1 | 0.5122 |
| cosine_precision@3 | 0.4255 |
| cosine_precision@5 | 0.3333 |
| cosine_precision@10 | 0.2 |
| cosine_recall@1 | 0.1949 |
| cosine_recall@3 | 0.4714 |
| cosine_recall@5 | 0.6096 |
| cosine_recall@10 | 0.7022 |
| cosine_ndcg@10 | 0.5903 |
| cosine_mrr@10 | 0.6585 |
| cosine_map@100 | 0.5016 |
text_a and text_b| text_a | text_b | |
|---|---|---|
| type | string | string |
| details |
|
|
| text_a | text_b |
|---|---|
The thermal expansion of the oceans, compounded by melting glaciers, resulted in the highest global sea level on record in 2015. | Since the last glacial maximum about 20,000 years ago, the sea level has risen by more than 125 metres (410 ft), with rates varying from less than a mm/year to 40+ mm/year, as a result of melting ice sheets over Canada and Eurasia. |
The thermal expansion of the oceans, compounded by melting glaciers, resulted in the highest global sea level on record in 2015. | This acceleration is due mostly to human-caused global warming, which is driving thermal expansion of seawater and the melting of land-based ice sheets and glaciers. |
The thermal expansion of the oceans, compounded by melting glaciers, resulted in the highest global sea level on record in 2015. | Between 1993 and 2018, thermal expansion of the oceans contributed 42% to sea level rise; the melting of temperate glaciers, 21%; Greenland, 15%; and Antarctica, 8%. |
MultipleNegativesRankingLoss with these parameters:
1{
2 "scale": 20.0,
3 "similarity_fct": "cos_sim"
4}text_a and text_b| text_a | text_b | |
|---|---|---|
| type | string | string |
| details |
|
|
| text_a | text_b |
|---|---|
Postma's model contains many simple errors; in no way does Postma undermine the existence or necessity of the greenhouse effect. | Since it is absurd to have no logical method for settling on one hypothesis amongst an infinite number of equally data-compliant hypotheses, we should choose the simplest theory: "Either science is irrational [in the way it judges theories and predictions probable] or the principle of simplicity is a fundamental synthetic a priori truth." |
Postma's model contains many simple errors; in no way does Postma undermine the existence or necessity of the greenhouse effect. | Thus, complex hypotheses must predict data much better than do simple hypotheses before researchers reject the simple hypotheses. |
Postma's model contains many simple errors; in no way does Postma undermine the existence or necessity of the greenhouse effect. | Minimum description length Minimum message length – Formal information theory restatement of Occam's Razor Newton's flaming laser sword Philosophical razor – Principle or rule of thumb that allows one to eliminate unlikely explanations for a phenomenon Philosophy of science – The philosophical study of the assumptions, foundations, and implications of science Simplicity "Ockham's razor does not say that the more simple a hypothesis, the better." |
MultipleNegativesRankingLoss with these parameters:
1{
2 "scale": 20.0,
3 "similarity_fct": "cos_sim"
4}eval_strategy: epochper_device_train_batch_size: 16per_device_eval_batch_size: 16learning_rate: 2e-05num_train_epochs: 4warmup_ratio: 0.1load_best_model_at_end: Trueoverwrite_output_dir: Falsedo_predict: Falseeval_strategy: epochprediction_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: 2e-05weight_decay: 0.0adam_beta1: 0.9adam_beta2: 0.999adam_epsilon: 1e-08max_grad_norm: 1.0num_train_epochs: 4max_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: 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: Trueignore_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: proportional| Epoch | Step | Training Loss | Validation Loss | climate-val-simpler_cosine_ndcg@10 |
|---|---|---|---|---|
| 1.0 | 208 | 0.6011 | 0.6501 | 0.5733 |
| 2.0 | 416 | 0.355 | 0.6107 | 0.5824 |
| 3.0 | 624 | 0.2594 | 0.6122 | 0.5843 |
| 4.0 | 832 | 0.2073 | 0.6029 | 0.5903 |
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}