Add new SentenceTransformer model.

a23af80 verified 4 months ago

28.5 kB

	---
	base_model: distilbert/distilroberta-base
	datasets: []
	language: []
	library_name: sentence-transformers
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:3394
	- loss:MultipleNegativesRankingLoss
	widget:
	- source_sentence: 'As senna, Watch your positioning at all times so she cannot land
	crucial spells on you.


	Keep the minion wave closer to your side of the map to force her to over-extend
	for farm.


	Senna is quite weak early, you might be able to abuse that to gain an early health
	advantage. But if you can’t, don’t worry.


	Focus on farming early on if you can’t get kills. Try to poke her down often:
	whenever she uses her Q, try and use yours.

	'
	sentences:
	- As riven, Riven will begin to fall as the game continues, so she will have to
	accumulate an early lead on. She should rely on peaks to win the game. She will
	have a lot of articles during this phase of the game, which will let her dry a
	lot of damage if she manages to catch an enemy. Level 16 is a massive power peak,
	which means that she can completely decimate the enemies that are joined together.
	She will have to find flanks on the enemy frequently.
	- As senna, Watch your position at all times so it can't put crucial spells on you.
	Keep the minion wave closer to your side of the map to force it to overtake for
	the farm. Senna is weak enough early, you might be able to abuse it to gain an
	early health benefit. But if you can, don't worry. Focus on agriculture early
	if you can't get killed.
	- Against zac, Zac really has long cooling times on his engagement abilities. As
	a result, he can really use them as a tool of engagement or escape only once in
	a fight. If you know that he is without these abilities- try to fight him. Placing
	the vision around his entrances into the jungle and into the river will reduce
	his ability to get successful ganks. Note that Zac can gank from afar with his
	E however. The easiest way for Zac to kill the Crab Scuttle is by using his E.
	If you see him killing the Crab Scuttle, it will usually mean that his E is on
	cooldown and he will miss an escape tool.
	- source_sentence: 'As teemo, Use your t to prevent the enemy ADC from doing anything
	in this game. Always focus on the champion that is close to your carry for maximal
	effectiveness.


	Try to lure enemies into fighting near your Ultimate t infested areas. It will
	allow you to auto-win a major fight due to the amount of damage your Ultimate
	t traps do.


	Ensure that your Ultimate t traps are in the choke points during neutral objective
	fights. This will make it really hard for the enemy team to do anything in the
	fight.'
	sentences:
	- As taric, Taric will have his Q maxed at level 9. This means that he will heal
	a lot during a fight as long as he has enough mana to constantly use the ability.
	At level 11, Taric will have two points in his Ultimate R. This means that he
	will be able to use the ability frequently, which will make him powerful enough
	in team combat. At level thirteen, Taric will have two of his maximum capabilities
	out. This means that he will excel in team combat and will be successfully peeled
	for his port.
	- As annie, Look for aggressive parts at level 2 that you are stronger than it at
	level 2. Look for aggressive parts when its dizziness is down. Fight it after
	it uses its dizziness that you will win a battle with it. After you have obtained
	your first element, just keep pushing it in and get turn plates. Look for roaming
	opportunities when possible.
	- As teemo, Use your t to prevent the ADC enemy from doing anything in this game.
	Always focus on the champion who is close to your door for maximum efficiency.
	Try to attract enemies into the fight near your infested areas Ultimate t. It
	will allow you to self-win a major fight due to the amount of damage your Ultimate
	t traps do. Make sure your Ultimate t traps are in the choking points during neutral
	objective combats. This will make it really difficult for the enemy team to do
	anything in the fight.
	- source_sentence: 'As senna, Just like in the mid-game, you should stick with your
	Support throughout the later parts of the game. Do not go around the map alone
	as you will die easily.


	Do not play super aggressive in team fights. Just kite and auto-attack the nearest
	enemy champion. If you walk too far forward, the enemy will focus you and take
	you down.


	Continue to kite in team fights and consistently adapt your positioning. Avoid
	standing still in fights as you’ll be an easy target.'
	sentences:
	- As senna, Just like in the middle of the game, you have to stay with your support
	throughout the later parts of the game. Do not go around the map alone because
	you will die easily. Do not play super aggressive in team fights. Just kite and
	automatic attack the nearest enemy champion. If you walk too far forward, the
	enemy will concentrate and down you. Continue kiteing in team fights and systematically
	adapt your positioning.
	- As udyr, You are really good at securing the goals in early play. Look to secure
	the goals whenever they are. Find a healthy balance between the glove of your
	allies, the agriculture of your jungle and safety goals. Make sure you don't fall
	behind in XP trying to gank constantly. Look several times at the gank tracks
	that are wider or Flashless. Your champion is very good at several times the glove
	tracks over and over again.
	- As bard, Once you reach this stage, you should focus more on roaming and the impact
	of the map with your Ultimate R & E. When you keep in depth, make sure to keep
	your E ready to escape any sticky situation. Before leaving your track, drop some
	W shrines here and there. This will help your ADC stay healthy, especially when
	they are against a poke track. Always try to lure enemy travellers to push and
	use the wall behind them to E and then stun them with your Q. The association
	with your Jungler/other travelers will give maximum efficiency and enemies generally
	do not plan such coins.
	- source_sentence: 'Against trundle, When Trundle activates his n, back away immediately.
	He will become much tankier and your tanks will be weak. Re-engage when his Subjugate
	Subjugate n has cooled off.


	He has great pick potential in the mid game with his e. Avoid standing too far
	forward as he may push you towards his team and then all-in you.


	Poke is your best friend against Trundle. Getting him really low makes it near
	impossible for him to all-in or fight your team.'
	sentences:
	- As kalista, If she has a support that can complete her escarment power, she will
	easily take the level path one himself. Kalista's passive does so so that she
	can easily avoid the CC abilities especially if it turns out that it is a skill
	shot. It also allows her to jump in and out of nonwarded brushes to drop the vision
	on the enemies. Once she gets her Ultimate x, her down pursuit potential becomes
	unmatched.
	- Against trundle, When Trundle activates his n, backs off immediately. He will
	become much more tankier and your tanks will be weak. Re-engagement when his Subjugate
	Subjugate n has cooled. He has great potential of choice in the middle of the
	match with his e. Avoid standing too far ahead that he can push you towards his
	team and then all of you. Poke is your best friend against Trundle.
	- As viego, Viego is decent enough during the team fights because of his W and Q.
	His life flight will help him during the full fights, and his Ultimate R will
	act as a decent finish movement. Viego will win a massive power peak once he has
	two points in his Ultimate R. He can run squishy targets, execute them and use
	his Passive for much longer than any other champion in his situation. He will
	have his basic elements through this phase of the game. He should be able to flatten
	a lot of damage and catch the out-of-guard enemies with his W. His E should help
	him shoot several ganks as well.
	- source_sentence: 'As mordekaiser, Another point in his Ultimate R will allow him
	to 1 v 1 target quite frequently. He should be able to secure picks even now.


	He is pretty decent during late-game fights, as his AoE abilities will hurt a
	lot. His survivability is quite appreciable as well.


	His tankiness will be massive during this phase of the game. He should be focused
	on absorbing a lot of damage for the enemy team while simultaneously picking off
	enemies when possible.'
	sentences:
	- As karma, Unlike most champions, Karma unlocks her Ultimate has at level 1. This
	means that at level 6, she doesn't have as much pressure compared to many other
	champions especially if they re-level 6 power peak is very strong. When Karmas
	d is down, she is vulnerable to attack and will need to play safely while he is
	on the cooldown. His Q can easily be avoided if the enemy builds a huge wave or
	positions properly.
	- As mordekaiser, Another point in his Ultimate R will allow him to 1 v 1 target
	quite frequently. He should be able to secure the choices even now. He is quite
	decent during the fights at the end of the game, because his AoE abilities will
	do a lot of harm. His survival is just as appreciable. His tankness will be massive
	during this phase of the game. He should be focused on absorbing a lot of damage
	for the enemy team while simultaneously dropping enemies when possible.
	- As renekton, If you can get a murder or two, you can snowball your lead quite
	quickly. Every time your Ultimate t is up, you can look for aggressive games to
	try to kill the enemy. Your Ultimate t is an excellent trading tool that makes
	you much stronger. Keep the minion wave even or slightly closer to your side of
	the map early. This will allow you to run the enemy down while protecting yourself
	from the ganks. If you keep pushing when you are not forward, you will be unable
	to run the enemy down and you will be an easy target for the enemy Jungler.
	---

	# SentenceTransformer based on distilbert/distilroberta-base

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-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: [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) <!-- at revision fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	<!-- - Language: Unknown -->
	<!-- - License: Unknown -->

	### 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: RobertaModel
	(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})
	)
	```

	## 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("avinot/distilroberta-base-LoL-Champions")
	# Run inference
	sentences = [
	'As mordekaiser, Another point in his Ultimate R will allow him to 1 v 1 target quite frequently. He should be able to secure picks even now.\n\nHe is pretty decent during late-game fights, as his AoE abilities will hurt a lot. His survivability is quite appreciable as well.\n\nHis tankiness will be massive during this phase of the game. He should be focused on absorbing a lot of damage for the enemy team while simultaneously picking off enemies when possible.',
	'As mordekaiser, Another point in his Ultimate R will allow him to 1 v 1 target quite frequently. He should be able to secure the choices even now. He is quite decent during the fights at the end of the game, because his AoE abilities will do a lot of harm. His survival is just as appreciable. His tankness will be massive during this phase of the game. He should be focused on absorbing a lot of damage for the enemy team while simultaneously dropping enemies when possible.',
	'As renekton, If you can get a murder or two, you can snowball your lead quite quickly. Every time your Ultimate t is up, you can look for aggressive games to try to kill the enemy. Your Ultimate t is an excellent trading tool that makes you much stronger. Keep the minion wave even or slightly closer to your side of the map early. This will allow you to run the enemy down while protecting yourself from the ganks. If you keep pushing when you are not forward, you will be unable to run the enemy down and you will be an easy target for the enemy Jungler.',
	]
	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]
	```

	<!--
	### Direct Usage (Transformers)

	<details><summary>Click to see the direct usage in Transformers</summary>

	</details>
	-->

	<!--
	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

	</details>
	-->

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	### Out-of-Scope Use

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	## Bias, Risks and Limitations

	What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.
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	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
	-->

	## Training Details

	### Training Dataset

	#### Unnamed Dataset


	* Size: 3,394 training samples
	* Columns: <code>sentence_0</code> and <code>sentence_1</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence_0 \| sentence_1 \|
	\|:--------\|:------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 6 tokens</li><li>mean: 125.49 tokens</li><li>max: 272 tokens</li></ul> \| <ul><li>min: 27 tokens</li><li>mean: 109.21 tokens</li><li>max: 231 tokens</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \|
	\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>As sivir, Just like in the mid-game, you should stick with your Support throughout the later parts of the game. Do not go around the map alone as you will die easily.<br><br>Do not play super aggressive in team fights. Just kite and auto-attack the nearest enemy champion. If you walk too far forward, the enemy will focus you and take you down.<br><br>Continue to kite in team fights and consistently adapt your positioning. Avoid standing still in fights as you’ll be an easy target.</code> \| <code>As sivir, Just like in the middle of the game, you have to stay with your support throughout the later parts of the game. Do not go around the map alone because you will die easily. Do not play super aggressive in team fights. Just kite and automatic attack the nearest enemy champion. If you walk too far forward, the enemy will concentrate and down you. Continue kiteing in team fights and systematically adapt your positioning.</code> \|
	\| <code>As nunu, After going in with your Ultimate R, be prepared to fall back and peel for your allies in late-game team fights.<br><br>Play around your Ultimate R in the later parts of the game. Avoid fighting unless your Ultimate R is up. Fighting without it will make the late-game team fights much harder. Delay fights and be prepared to disengage if it’s still on cooldown.<br><br>To make getting on the enemy backline easier, group with your team but stay off to the side. If you flank from an unwarded bush, the enemy will find it harder to react to your all-in. Avoid splitting or being away from your team in the late game as the enemy will force a fight while you’re gone.</code> \| <code>As nunu, After entering with your Ultimate R, be ready to fold and peel for your allies in the team fights at the end of the game. Play around your Ultimate R in the later parts of the game. Avoid fighting unless your Ultimate R is standing. Fighting without it will make the team fight a lot harder at the end of the game. Delaying the fights and being ready to disengage if it is still about to cool off. To make the enemy's attack easier, group with your team but stay away from the side. If you flank a bush not awarded, the enemy will find it harder to react to your all-in. Avoid separating or being away from your team in the end of the game as the enemy will force a fight while you are gone.</code> \|
	\| <code>As darius, Darius is one of the strongest early game champions in the game. You can use this advantage to gain an early lead.<br><br>Extended trades work in the favour of Darius thanks to his Passive. He will win almost every auto-attack battle. This makes him a great duelist in low ELO as many players like to constantly fight.<br><br>Is really good in team fights thanks to his Ultimate e which can be reset if he gets the killing blow with it.</code> \| <code>As darius, Darius is one of the first strongest game champions in the game. You can use this advantage to gain an early lead. Extended trades work in favor of Darius thanks to his passive. He will win almost all automatic attack battles. This makes him a great duelist in the bottom ELO as many players like to fight constantly. Is really good in team fights thanks to his Ultimate e that can be reset if he gets the shot of death with her.</code> \|
	* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `num_train_epochs`: 10
	- `multi_dataset_batch_sampler`: round_robin

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: no
	- `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
	- `num_train_epochs`: 10
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.0
	- `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
	- `restore_callback_states_from_checkpoint`: 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`: False
	- `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, 'non_blocking': False, '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_eval_metrics`: False
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: round_robin

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \|
	\|:------:\|:----:\|:-------------:\|
	\| 2.3474 \| 500 \| 1.9312 \|
	\| 4.6948 \| 1000 \| 0.0145 \|
	\| 7.0423 \| 1500 \| 0.0023 \|
	\| 9.3897 \| 2000 \| 0.0003 \|


	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.0.1
	- Transformers: 4.41.2
	- PyTorch: 2.3.0+cu121
	- Accelerate: 0.31.0
	- Datasets: 2.20.0
	- Tokenizers: 0.19.1

	## Citation

	### BibTeX

	#### Sentence Transformers
	```bibtex
	@inproceedings{reimers-2019-sentence-bert,
	title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
	author = "Reimers, Nils and Gurevych, Iryna",
	booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
	month = "11",
	year = "2019",
	publisher = "Association for Computational Linguistics",
	url = "https://arxiv.org/abs/1908.10084",
	}
	```

	#### MultipleNegativesRankingLoss
	```bibtex
	@misc{henderson2017efficient,
	title={Efficient Natural Language Response Suggestion for Smart Reply},
	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},
	year={2017},
	eprint={1705.00652},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```

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