---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:134975
- loss:CoSENTLoss
- dataset_size:134934
base_model: thenlper/gte-base
datasets: []
widget:
- source_sentence: 9 postcolonial studies The Human in the Anthropocene If the problem
of global warming or climate change had not burst in on us through the 2007 Report
of the Intergovernmental Panel on Climate Change (IPCC), globalization would have
been perhaps the most important theme stoking our thoughts about being human.
sentences:
- 'Baltimore, MD: Johns Hopkins University Press, 1993.'
- Publisher contact information may be obtained at http://www.jstor.org/journals/sage.html.
- "He Decolonizing the Cosmopolitan Geospatial Imaginary of the Anthropocene Pivot\
\ 7.1 159 suggests that in discussion of the anthropogenic climate change and\
\ global warming, one has to think of these things simultaneously: â\x80\x9Cthe\
\ human-human and the nonhuman-humanâ\x80\x9D (11)."
- source_sentence: "1-18 (Article) Published by The Johns Hopkins University Press\
\ DOI: 10.1353/nlh.2012.0007 For additional information about this article Access\
\ provided by Australian National University (2 May 2013 23:47 GMT) http://muse.jhu.edu/journals/nlh/summary/v043/43.1.chakrabarty.html\
\ New Literary History, 2012, 43: 1â\x80\x9318 Postcolonial Studies and the Challenge\
\ of Climate Change Dipesh Chakrabarty For Homi K. Bhabha H owever we come to\
\ the question of postcolonial studies at this historical juncture, there are\
\ two phenomena, both topics of public debate since the early 1990s, that none\
\ of us can quite escape in our personal and collective lives at present: globalization\
\ and global warming."
sentences:
- What marks the rise of the Anthropocene proper is the fact that current geological
transformations are dominated by human action.
- Postcolonial studies and the challenge of climate change.
- How do we think of this collective human agency in the era of the Anthropocene?
- source_sentence: "Chakrabarty thus appeals for â\x80\x98non-ontological ways of\
\ thinking the humanâ\x80\x99 (2012: 13) to bring about this needed interpretive\
\ stretching."
sentences:
- "Arendt, H, (1998) The human condition, Chicago: The University of Chicago Arias-Ââ\x80\
\x90Maldonado, M. (2015) â\x80\x98Spelling the end of nature?"
- "â\x80\x9CPostcolonial Studies and the Challenge of Climate Change.â\x80\x9D New\
\ Literary History 43.1 (2012): 1â\x80\x9318."
- We need nonontological ways of thinking the human.
- source_sentence: "23 Chakrabarty, â\x80\x9CPostcolonial Studiesâ\x80\x9D, 11; italics\
\ in original."
sentences:
- Chakrabarty, D. Postcolonial Studies and the Challenge of Climate Change.
- "In this article, we adopt the term Anthropocene precisely to refer to these processes\
\ and phenomena, and to the associated urgency of ï¬\x81nding new ways to inhabit\
\ this present-day (and future) epoch in which we perceive Earth no longer as\
\ the framework for human action, but precisely as participating in (and increasingly\
\ partaking in the conï¬\x81guring of) that action [23] (p. 42)."
- Postcolonial Studies and the Challenge of Climate Change Dipesh Chakrabarty New
Literary History, Volume 43, Number 1, Winter 2012, pp.
- source_sentence: And then comes the figure of the human in the age of the Anthropocene,
the era when humans act as a geological force on the planet, changing its climate
for millennia to come.
sentences:
- "â\x80\x98Anthropoceneâ\x80\x99 means, after all, â\x80\x98new Man time.â\x80\x99\
\ For, while the Anthropocene, as a name, claims a generalised human agency responsible\
\ for the myriad ecological crises gathered under its auspice, it is simply not\
\ the case that, as Ghosh argues, â\x80\x9Cevery human being, past and present,\
\ has contributed to the present cycle of climate changeâ\x80\x9D (2016, 115)."
- 'Minneapolis: University of Minnesota Press, 2007.'
- 'Baltimore, MD: Johns Hopkins University Press, 1993.'
pipeline_tag: sentence-similarity
---
# SentenceTransformer based on thenlper/gte-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [thenlper/gte-base](https://huggingface.co/thenlper/gte-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [thenlper/gte-base](https://huggingface.co/thenlper/gte-base)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
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})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'And then comes the figure of the human in the age of the Anthropocene, the era when humans act as a geological force on the planet, changing its climate for millennia to come.',
'â\x80\x98Anthropoceneâ\x80\x99 means, after all, â\x80\x98new Man time.â\x80\x99 For, while the Anthropocene, as a name, claims a generalised human agency responsible for the myriad ecological crises gathered under its auspice, it is simply not the case that, as Ghosh argues, â\x80\x9cevery human being, past and present, has contributed to the present cycle of climate changeâ\x80\x9d (2016, 115).',
'Minneapolis: University of Minnesota Press, 2007.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 134,934 training samples
* Columns: inp1, inp2, and score
* Approximate statistics based on the first 1000 samples:
| | inp1 | inp2 | score |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------|
| type | string | string | float |
| details | - min: 8 tokens
- mean: 38.09 tokens
- max: 512 tokens
| - min: 8 tokens
- mean: 32.43 tokens
- max: 512 tokens
| - min: -1.0
- mean: -0.8
- max: 1.0
|
* Samples:
| inp1 | inp2 | score |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| Following the lead of John Guillory in Cultural Capital: The Problem of Literary Canon Formation, I would argue that such theoretical arguments characteristically concern an âimaginary canonââimaginary in that there is no speciï¬cally deï¬ned body of works or authors that make up such a canon. | âBrooksâs theory,â guillory writes in Cultural Capital: The Problem of Liter- ary Canon Formation (Chicago: Univ. | 1.0 |
| Cultural Capital: The Problem of Literary Canon Formation. | âBrooksâs theory,â guillory writes in Cultural Capital: The Problem of Liter- ary Canon Formation (Chicago: Univ. | 1.0 |
| A partic- ularly good example of the complex operations of critical attention and peda- gogical appropriation occurs with Zora Neale Hurstonâs Their Eyes Were Watching God. | Similarly, in her article comparing the image patterns in Zora Neale Hurstonâs Their Eyes Were Watching God and Beloved, Glenda B. Weathers also observes the dichotomous function of the trees in Beloved and argues, âThey posit knowledge of both good and evilâ (2005, 201) for black Americans seek- ing freedom from slavery and oppression. | 1.0 |
* Loss: [CoSENTLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 2
- `warmup_ratio`: 0.1
- `fp16`: True
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
Click to expand
| Epoch | Step | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0119 | 100 | 2.2069 |
| 0.0237 | 200 | 2.3883 |
| 0.0119 | 100 | 1.8358 |
| 0.0237 | 200 | 1.974 |
| 0.0356 | 300 | 1.8488 |
| 0.0474 | 400 | 1.8799 |
| 0.0593 | 500 | 2.0132 |
| 0.0711 | 600 | 1.8831 |
| 0.0830 | 700 | 1.601 |
| 0.0948 | 800 | 2.0316 |
| 0.1067 | 900 | 1.9483 |
| 0.1185 | 1000 | 1.6585 |
| 0.1304 | 1100 | 1.7986 |
| 0.1422 | 1200 | 1.4978 |
| 0.1541 | 1300 | 1.6035 |
| 0.1660 | 1400 | 1.9908 |
| 0.1778 | 1500 | 1.2896 |
| 0.1897 | 1600 | 1.97 |
| 0.2015 | 1700 | 1.9622 |
| 0.2134 | 1800 | 1.4706 |
| 0.2252 | 1900 | 1.5162 |
| 0.2371 | 2000 | 1.6988 |
| 0.2489 | 2100 | 1.6552 |
| 0.2608 | 2200 | 1.7779 |
| 0.2726 | 2300 | 1.9001 |
| 0.2845 | 2400 | 1.7802 |
| 0.2963 | 2500 | 1.6582 |
| 0.3082 | 2600 | 1.377 |
| 0.3201 | 2700 | 1.473 |
| 0.3319 | 2800 | 1.441 |
| 0.3438 | 2900 | 1.8727 |
| 0.3556 | 3000 | 1.1545 |
| 0.3675 | 3100 | 1.7319 |
| 0.3793 | 3200 | 1.9862 |
| 0.3912 | 3300 | 1.467 |
| 0.4030 | 3400 | 2.125 |
| 0.4149 | 3500 | 2.0474 |
| 0.4267 | 3600 | 1.7078 |
| 0.4386 | 3700 | 1.7791 |
| 0.4505 | 3800 | 1.6368 |
| 0.4623 | 3900 | 1.4451 |
| 0.4742 | 4000 | 1.5612 |
| 0.4860 | 4100 | 1.3163 |
| 0.4979 | 4200 | 1.5675 |
| 0.5097 | 4300 | 1.2766 |
| 0.5216 | 4400 | 1.4506 |
| 0.5334 | 4500 | 0.9601 |
| 0.5453 | 4600 | 1.4118 |
| 0.5571 | 4700 | 1.3951 |
| 0.5690 | 4800 | 1.2048 |
| 0.5808 | 4900 | 1.1108 |
| 0.5927 | 5000 | 1.5696 |
| 0.6046 | 5100 | 1.4223 |
| 0.6164 | 5200 | 1.1789 |
| 0.6283 | 5300 | 1.1573 |
| 0.6401 | 5400 | 1.4457 |
| 0.6520 | 5500 | 1.6622 |
| 0.6638 | 5600 | 1.2699 |
| 0.6757 | 5700 | 1.0191 |
| 0.6875 | 5800 | 1.2764 |
| 0.6994 | 5900 | 0.8999 |
| 0.6046 | 5100 | 1.5085 |
| 0.6164 | 5200 | 1.3738 |
| 0.6283 | 5300 | 1.0537 |
| 0.6401 | 5400 | 1.3578 |
| 0.6520 | 5500 | 1.6301 |
| 0.6638 | 5600 | 1.091 |
| 0.6757 | 5700 | 0.9261 |
| 0.6875 | 5800 | 1.1276 |
| 0.6994 | 5900 | 0.7678 |
| 0.6047 | 5100 | 1.2021 |
| 0.6166 | 5200 | 0.8787 |
| 0.6284 | 5300 | 0.6169 |
| 0.6403 | 5400 | 0.9881 |
| 0.6521 | 5500 | 1.1844 |
| 0.6640 | 5600 | 1.032 |
| 0.6758 | 5700 | 0.8486 |
| 0.6877 | 5800 | 1.4845 |
| 0.6995 | 5900 | 1.4 |
| 0.7114 | 6000 | 0.9685 |
| 0.7233 | 6100 | 0.9288 |
| 0.7351 | 6200 | 1.4682 |
| 0.7470 | 6300 | 0.6551 |
| 0.7588 | 6400 | 0.5513 |
| 0.7707 | 6500 | 0.6092 |
| 0.7825 | 6600 | 1.3235 |
| 0.7944 | 6700 | 0.4917 |
| 0.8063 | 6800 | 0.8944 |
| 0.8181 | 6900 | 0.9298 |
| 0.8300 | 7000 | 1.1134 |
| 0.8418 | 7100 | 0.8254 |
| 0.8537 | 7200 | 1.3363 |
| 0.8655 | 7300 | 0.6571 |
| 0.8774 | 7400 | 0.8209 |
| 0.8893 | 7500 | 0.6508 |
| 0.9011 | 7600 | 1.1972 |
| 0.9130 | 7700 | 1.1095 |
| 0.9248 | 7800 | 0.8772 |
| 0.9367 | 7900 | 1.0623 |
| 0.9485 | 8000 | 0.6073 |
| 0.9604 | 8100 | 0.8292 |
| 0.9723 | 8200 | 0.6765 |
| 0.9841 | 8300 | 0.5103 |
| 0.9960 | 8400 | 1.0618 |
| 1.0078 | 8500 | 0.5134 |
| 1.0197 | 8600 | 0.5203 |
| 1.0315 | 8700 | 0.6634 |
| 1.0434 | 8800 | 0.6644 |
| 1.0553 | 8900 | 0.7459 |
| 1.0671 | 9000 | 0.5969 |
| 1.0790 | 9100 | 0.5473 |
| 1.0908 | 9200 | 0.5495 |
| 1.1027 | 9300 | 0.5093 |
| 1.1145 | 9400 | 0.7049 |
| 1.1264 | 9500 | 0.726 |
| 1.1382 | 9600 | 0.6512 |
| 1.1501 | 9700 | 0.5121 |
| 1.1620 | 9800 | 0.5977 |
| 1.1738 | 9900 | 0.4933 |
| 1.1857 | 10000 | 0.8585 |
| 1.1975 | 10100 | 0.2955 |
| 1.2094 | 10200 | 0.6972 |
| 1.2212 | 10300 | 0.454 |
| 1.2331 | 10400 | 1.1057 |
| 1.2450 | 10500 | 0.9724 |
| 1.2568 | 10600 | 0.3057 |
| 1.2687 | 10700 | 0.5967 |
| 1.2805 | 10800 | 0.7332 |
| 1.2924 | 10900 | 0.5382 |
| 1.3042 | 11000 | 0.625 |
| 1.3161 | 11100 | 0.5354 |
| 1.3280 | 11200 | 0.4289 |
| 1.3398 | 11300 | 0.4243 |
| 1.3517 | 11400 | 0.6902 |
| 1.3635 | 11500 | 0.4248 |
| 1.3754 | 11600 | 0.3743 |
| 1.3872 | 11700 | 0.5463 |
| 1.3991 | 11800 | 0.8413 |
| 1.4110 | 11900 | 0.4748 |
| 1.4228 | 12000 | 0.56 |
| 1.4347 | 12100 | 0.9269 |
| 1.4465 | 12200 | 0.4668 |
| 1.4584 | 12300 | 0.4842 |
| 1.4702 | 12400 | 0.5172 |
| 1.4821 | 12500 | 0.4498 |
| 1.4940 | 12600 | 0.4695 |
| 1.5058 | 12700 | 0.2144 |
| 1.5177 | 12800 | 0.8002 |
| 1.5295 | 12900 | 0.4022 |
| 1.5414 | 13000 | 0.4491 |
| 1.5532 | 13100 | 0.4798 |
| 1.5651 | 13200 | 0.7489 |
| 1.5770 | 13300 | 0.6108 |
| 1.5888 | 13400 | 0.3806 |
| 1.6007 | 13500 | 0.4164 |
| 1.6125 | 13600 | 0.6362 |
| 1.6244 | 13700 | 0.4773 |
| 1.6362 | 13800 | 0.4875 |
| 1.6481 | 13900 | 0.5577 |
| 1.6599 | 14000 | 0.3318 |
| 1.6718 | 14100 | 0.2959 |
| 1.6837 | 14200 | 0.3168 |
| 1.6955 | 14300 | 0.403 |
| 1.7074 | 14400 | 0.6553 |
| 1.7192 | 14500 | 0.5814 |
| 1.7311 | 14600 | 0.3407 |
| 1.7429 | 14700 | 0.3985 |
| 1.7548 | 14800 | 0.406 |
| 1.7667 | 14900 | 0.5986 |
| 1.7785 | 15000 | 0.7694 |
| 1.7904 | 15100 | 0.5025 |
| 1.8022 | 15200 | 0.7199 |
| 1.8141 | 15300 | 0.4215 |
| 1.8259 | 15400 | 0.5484 |
| 1.8378 | 15500 | 0.3551 |
| 1.8497 | 15600 | 0.3572 |
| 1.8615 | 15700 | 0.3536 |
| 1.8734 | 15800 | 0.5116 |
| 1.8852 | 15900 | 0.7094 |
| 1.8971 | 16000 | 0.4402 |
| 1.9089 | 16100 | 0.4095 |
| 1.9208 | 16200 | 0.2173 |
| 1.9327 | 16300 | 0.6058 |
| 1.9445 | 16400 | 0.7796 |
| 1.9564 | 16500 | 0.5642 |
| 1.9682 | 16600 | 0.3085 |
| 1.9801 | 16700 | 0.4308 |
| 1.9919 | 16800 | 0.3712 |