Add new CrossEncoder model
Browse files- README.md +104 -63
- model.safetensors +1 -1
README.md
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- cross-encoder
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- reranker
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- generated_from_trainer
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- dataset_size:
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- loss:BinaryCrossEntropyLoss
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base_model: Alibaba-NLP/gte-reranker-modernbert-base
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pipeline_tag: text-ranking
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library_name: sentence-transformers
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metrics:
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type: NanoMSMARCO_R100
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metrics:
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- type: map
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value: 0.
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name: Map
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- type: mrr@10
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value: 0.
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name: Mrr@10
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- type: ndcg@10
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value: 0.
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name: Ndcg@10
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- task:
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type: cross-encoder-reranking
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type: NanoNFCorpus_R100
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metrics:
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- type: map
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value: 0.
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name: Map
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- type: mrr@10
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value: 0.
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name: Mrr@10
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- type: ndcg@10
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value: 0.
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name: Ndcg@10
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- task:
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type: cross-encoder-reranking
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type: NanoNQ_R100
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metrics:
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- type: map
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value: 0.
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name: Map
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- type: mrr@10
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value: 0.
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name: Mrr@10
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- type: ndcg@10
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value: 0.
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name: Ndcg@10
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- task:
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type: cross-encoder-nano-beir
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type: NanoBEIR_R100_mean
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metrics:
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- type: map
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value: 0.
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name: Map
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- type: mrr@10
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value: 0.
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name: Mrr@10
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- type: ndcg@10
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value: 0.
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name: Ndcg@10
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---
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# CrossEncoder based on Alibaba-NLP/gte-reranker-modernbert-base
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This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [Alibaba-NLP/gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
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## Model Details
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### Model Description
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- **Model Type:** Cross Encoder
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- **Base model:** [Alibaba-NLP/gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) <!-- at revision f7481e6055501a30fb19d090657df9ec1f79ab2c -->
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- **Maximum Sequence Length:**
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- **Number of Output Labels:** 1 label
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-
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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from sentence_transformers import CrossEncoder
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# Download from the 🤗 Hub
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model = CrossEncoder("
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# Get scores for pairs of texts
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pairs = [
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['Ik ben op zoek naar info over magneetcontacten, die standaard deuren open houden, maar in geval van brand contact lossen en ervoor zorgen dat deuren sluiten. worden deze contacten gevoed vanuit de brandcentrale, of vanuit een voeding 230V AC , die geschakeld wordt vanuit de centrale? ', '
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scores = model.predict(pairs)
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print(scores.shape)
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# Or rank different texts based on similarity to a single text
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ranks = model.rank(
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)
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# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
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| Metric | NanoMSMARCO_R100 | NanoNFCorpus_R100 | NanoNQ_R100 |
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|:------------|:---------------------|:---------------------|:---------------------|
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| map | 0.
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| mrr@10 | 0.
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| **ndcg@10** | **0.
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#### Cross Encoder Nano BEIR
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| Metric | Value |
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|:------------|:---------------------|
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| map | 0.
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| mrr@10 | 0.
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| **ndcg@10** | **0.
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<!--
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## Bias, Risks and Limitations
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### Training Dataset
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####
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* Samples:
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|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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| <code>
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| <code>
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| <code>
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* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
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```json
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{
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"activation_fn": "torch.nn.modules.linear.Identity",
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"pos_weight":
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}
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```
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#### Non-Default Hyperparameters
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- `per_device_train_batch_size`: 16
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- `learning_rate`:
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- `warmup_steps`: 0.1
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- `bf16`: True
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- `eval_strategy`:
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- `push_to_hub`: True
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- `hub_model_id`:
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- `load_best_model_at_end`: True
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#### All Hyperparameters
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- `per_device_train_batch_size`: 16
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- `num_train_epochs`: 3
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- `max_steps`: -1
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- `learning_rate`:
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- `lr_scheduler_type`: linear
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- `lr_scheduler_kwargs`: None
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- `warmup_steps`: 0.1
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- `disable_tqdm`: False
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- `project`: huggingface
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- `trackio_space_id`: trackio
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- `eval_strategy`:
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- `per_device_eval_batch_size`:
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- `prediction_loss_only`: True
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- `eval_on_start`: False
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- `eval_do_concat_batches`: True
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- `enable_jit_checkpoint`: False
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- `push_to_hub`: True
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- `hub_private_repo`: None
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- `hub_model_id`:
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- `hub_strategy`: every_save
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- `hub_always_push`: False
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- `hub_revision`: None
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</details>
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### Training Logs
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| Epoch
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|:-------:|:------:|:-------------:|:------------------------:|:-------------------------:|:--------------------:|:--------------------------:|
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* The bold row denotes the saved checkpoint.
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- cross-encoder
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- reranker
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- generated_from_trainer
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- dataset_size:2277
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- loss:BinaryCrossEntropyLoss
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base_model: Alibaba-NLP/gte-reranker-modernbert-base
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datasets:
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- amanwithaplan/arcade-reranker-data
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pipeline_tag: text-ranking
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library_name: sentence-transformers
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metrics:
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type: NanoMSMARCO_R100
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metrics:
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- type: map
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value: 0.5976
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name: Map
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- type: mrr@10
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value: 0.5901
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name: Mrr@10
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- type: ndcg@10
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value: 0.656
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name: Ndcg@10
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- task:
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type: cross-encoder-reranking
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type: NanoNFCorpus_R100
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metrics:
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- type: map
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value: 0.4056
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name: Map
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- type: mrr@10
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value: 0.6538
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name: Mrr@10
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- type: ndcg@10
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value: 0.4606
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name: Ndcg@10
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- task:
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type: cross-encoder-reranking
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type: NanoNQ_R100
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metrics:
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- type: map
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value: 0.6834
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name: Map
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- type: mrr@10
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value: 0.7047
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name: Mrr@10
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- type: ndcg@10
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value: 0.7415
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name: Ndcg@10
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- task:
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type: cross-encoder-nano-beir
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type: NanoBEIR_R100_mean
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metrics:
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- type: map
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value: 0.5622
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name: Map
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- type: mrr@10
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value: 0.6495
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name: Mrr@10
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- type: ndcg@10
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value: 0.6194
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name: Ndcg@10
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---
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# CrossEncoder based on Alibaba-NLP/gte-reranker-modernbert-base
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+
This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [Alibaba-NLP/gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) on the [arcade-reranker-data](https://huggingface.co/datasets/amanwithaplan/arcade-reranker-data) dataset using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
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## Model Details
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### Model Description
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- **Model Type:** Cross Encoder
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- **Base model:** [Alibaba-NLP/gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) <!-- at revision f7481e6055501a30fb19d090657df9ec1f79ab2c -->
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- **Maximum Sequence Length:** 1024 tokens
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- **Number of Output Labels:** 1 label
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- **Training Dataset:**
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- [arcade-reranker-data](https://huggingface.co/datasets/amanwithaplan/arcade-reranker-data)
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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from sentence_transformers import CrossEncoder
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# Download from the 🤗 Hub
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model = CrossEncoder("idqo/arcade-reranker")
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# Get scores for pairs of texts
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pairs = [
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['Dus de einklant betaalt in totaal ook de onbalansprijs? Die dus bestaat uit het gewone tarief + verschil om tot onbalansprijs te komen?', 'Imbalance Price (Electricity Balancing Market): (a) each imbalance settlement period; (b) its imbalance price areas; (c) each imbalance direction. 4. The imbalance price for negative imbalance ...'],
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['Kun je een lijst geven van alle technische specificaties geven waar je rekening mee moet houden bij een transformator', 'Handmelders: Manuele brandmelders.'],
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['Wat weetje van dataloggers?', 'geen nummers op tellers. Facturatiegegevens?'],
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['Ik ben op zoek naar info over magneetcontacten, die standaard deuren open houden, maar in geval van brand contact lossen en ervoor zorgen dat deuren sluiten. worden deze contacten gevoed vanuit de brandcentrale, of vanuit een voeding 230V AC , die geschakeld wordt vanuit de centrale? ', 'Algemene beveiliging: Beveiligingscel: merk, type met zekering, type met relais en vermogenschakelaar, vermogen, relais.'],
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['Geef een overzicht van allemogelijke geldstromen in de verschillende transfer of energy regimes', 'Taksen, heffingen & accijnzen: Belastingen op elektriciteit.'],
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]
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scores = model.predict(pairs)
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print(scores.shape)
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# Or rank different texts based on similarity to a single text
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ranks = model.rank(
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'Dus de einklant betaalt in totaal ook de onbalansprijs? Die dus bestaat uit het gewone tarief + verschil om tot onbalansprijs te komen?',
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[
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'Imbalance Price (Electricity Balancing Market): (a) each imbalance settlement period; (b) its imbalance price areas; (c) each imbalance direction. 4. The imbalance price for negative imbalance ...',
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'Handmelders: Manuele brandmelders.',
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'geen nummers op tellers. Facturatiegegevens?',
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'Algemene beveiliging: Beveiligingscel: merk, type met zekering, type met relais en vermogenschakelaar, vermogen, relais.',
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'Taksen, heffingen & accijnzen: Belastingen op elektriciteit.',
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]
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)
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# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
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| Metric | NanoMSMARCO_R100 | NanoNFCorpus_R100 | NanoNQ_R100 |
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|:------------|:---------------------|:---------------------|:---------------------|
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| map | 0.5976 (+0.1081) | 0.4056 (+0.1446) | 0.6834 (+0.2638) |
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| mrr@10 | 0.5901 (+0.1126) | 0.6538 (+0.1540) | 0.7047 (+0.2780) |
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| **ndcg@10** | **0.6560 (+0.1155)** | **0.4606 (+0.1355)** | **0.7415 (+0.2409)** |
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#### Cross Encoder Nano BEIR
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| Metric | Value |
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|:------------|:---------------------|
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| map | 0.5622 (+0.1721) |
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| mrr@10 | 0.6495 (+0.1815) |
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| **ndcg@10** | **0.6194 (+0.1640)** |
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<!--
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## Bias, Risks and Limitations
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### Training Dataset
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#### arcade-reranker-data
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* Dataset: [arcade-reranker-data](https://huggingface.co/datasets/amanwithaplan/arcade-reranker-data) at [9e3c538](https://huggingface.co/datasets/amanwithaplan/arcade-reranker-data/tree/9e3c53897213f5842b1ae641563dd47d60b266ab)
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* Size: 2,277 training samples
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
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* Approximate statistics based on the first 1000 samples:
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| | sentence1 | sentence2 | label |
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|:--------|:------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
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| type | string | string | float |
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| details | <ul><li>min: 2 characters</li><li>mean: 103.67 characters</li><li>max: 558 characters</li></ul> | <ul><li>min: 13 characters</li><li>mean: 518.43 characters</li><li>max: 25528 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.31</li><li>max: 1.0</li></ul> |
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* Samples:
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| sentence1 | sentence2 | label |
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|:------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------|
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+
| <code>Kan je daarvoor op het internet kijken?</code> | <code>Er wordt gewerkt aan de datanetwerk-infrastructuur voor het project, waarbij energiemeters worden aangesloten op een fibernetwerk [5], [6], [7]. Er is een vaste internetverbinding aangevraagd, die door EXV wordt verzorgd en bij oplevering wordt overgedragen [4], [6], [8]. De Gateway van Tibo moet worden geïnstalleerd en verbonden met het netwerk, waarbij zowel 4G als een internetverbinding opties zijn, met redundantie als aanbeveling [2], [4], [6], [8]. Extravolt is verantwoordelijk voor het opzetten van de netwerkconfiguratie en stemt af met Tibo [4], [6]. Er wordt gekeken welke componenten verbinding maken met de cloud of internet, naast het EMS van Tibo, en hoe de batterij wordt uitgelezen door EXV voor de beschikbaarheidsgarantie, waarschijnlijk via VPN [2], [4], [6], [7], [8]. De meti</code> | <code>0.6666666666666666</code> |
|
| 251 |
+
| <code>Geef een generieke template voor verlichtingsberekingen in een lastenboek</code> | <code>Issue: nu blijkt dat er ook een datacenter aangesloten gaat moeten worden.<br>In welke mate kan dat om met +-10% -> dit gaat meer algemeen moeten bekeken worden.<br>- Hier is logica van antwoord dat de neteheerders zowiezo moeten voldoen aan een maximale marge van +-10% van hun uitgangsspanning.<br>- Die norm is aangeleverd door Infrabel.<br>- Dit betekent dat elektrische apparatuur die in het belgische net gezet wordt, moet bestand zijn aan schommelingen van +-10% Artikel 10. Variaties in spanning en frequ</code> | <code>0.0</code> |
|
| 252 |
+
| <code>Moet er oven brandhaspels altijd een noodverlichting voorzien worden? In welke norm vind ik hier info over</code> | <code>REFERENTIENORMEN \| Standard \| Description \|<br>\|---\|---\|<br>\| NBN 01 \| Woordenlijst voor de verlichtingskunde (2001) \|<br>\| NBN EN 60598 (1989) \| Elektrische verlichtingstoestellen (1989) \|<br>\| NBN EN 60598-2-2 \| Verlichtingstoestellen - Deel twee : Bijzondere regels - Sectie twee : Inbouw verlichtingstoestellen (1990) \|<br>\| Reeks NBN 60598 \| Verlichtingsarmaturen \|<br>\| NBN C 20-530 \| Beschermingsgraden gegeven door de omhulsels (IP-Code) (1992) + add (1000) \|<br>\| NBN EN 60001 \| Lampen en in - houders alsmede kalibers voor controle van uitwisselbaarheid & veiligheid 1-3 (2003) \|<br>\| NBN EN 1838 \| Toegepaste verlichtingstechniek - Noodverlichting (1999) \|<br>\| NBN L 14-001 & 002 \| Binnenverlichting van de gebouwen (1974) \|<br>\| NBN EN 60335-1 \| Huishoudelijke en soortgelijke elektrische toestellen - Veiligheid - Deel 1 : Algemene eisen (2003) \|<br>\| NBN EN 12464 \| Binnenverlichting (2008) \|<br>\| NBN EN 1838 \| Veiligheidsverlichting (2013) \|<br>\| EN 12464-1 \| werkplekverlichting (2002) \|<br>\| ISO 3864-1 en ISO 3864-4 \| (fot...</code> | <code>0.0643</code> |
|
| 253 |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
|
| 254 |
```json
|
| 255 |
{
|
| 256 |
"activation_fn": "torch.nn.modules.linear.Identity",
|
| 257 |
+
"pos_weight": null
|
| 258 |
+
}
|
| 259 |
+
```
|
| 260 |
+
|
| 261 |
+
### Evaluation Dataset
|
| 262 |
+
|
| 263 |
+
#### arcade-reranker-data
|
| 264 |
+
|
| 265 |
+
* Dataset: [arcade-reranker-data](https://huggingface.co/datasets/amanwithaplan/arcade-reranker-data) at [9e3c538](https://huggingface.co/datasets/amanwithaplan/arcade-reranker-data/tree/9e3c53897213f5842b1ae641563dd47d60b266ab)
|
| 266 |
+
* Size: 400 evaluation samples
|
| 267 |
+
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
|
| 268 |
+
* Approximate statistics based on the first 400 samples:
|
| 269 |
+
| | sentence1 | sentence2 | label |
|
| 270 |
+
|:--------|:-------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
|
| 271 |
+
| type | string | string | float |
|
| 272 |
+
| details | <ul><li>min: 27 characters</li><li>mean: 108.77 characters</li><li>max: 558 characters</li></ul> | <ul><li>min: 13 characters</li><li>mean: 404.07 characters</li><li>max: 11988 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.32</li><li>max: 1.0</li></ul> |
|
| 273 |
+
* Samples:
|
| 274 |
+
| sentence1 | sentence2 | label |
|
| 275 |
+
|:-----------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------|
|
| 276 |
+
| <code>Dus de einklant betaalt in totaal ook de onbalansprijs? Die dus bestaat uit het gewone tarief + verschil om tot onbalansprijs te komen?</code> | <code>Imbalance Price (Electricity Balancing Market): (a) each imbalance settlement period; (b) its imbalance price areas; (c) each imbalance direction. 4. The imbalance price for negative imbalance ...</code> | <code>0.3</code> |
|
| 277 |
+
| <code>Kun je een lijst geven van alle technische specificaties geven waar je rekening mee moet houden bij een transformator</code> | <code>Handmelders: Manuele brandmelders.</code> | <code>0.2222</code> |
|
| 278 |
+
| <code>Wat weetje van dataloggers?</code> | <code>geen nummers op tellers. Facturatiegegevens?</code> | <code>0.0256</code> |
|
| 279 |
+
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
|
| 280 |
+
```json
|
| 281 |
+
{
|
| 282 |
+
"activation_fn": "torch.nn.modules.linear.Identity",
|
| 283 |
+
"pos_weight": null
|
| 284 |
}
|
| 285 |
```
|
| 286 |
|
|
|
|
| 288 |
#### Non-Default Hyperparameters
|
| 289 |
|
| 290 |
- `per_device_train_batch_size`: 16
|
| 291 |
+
- `learning_rate`: 1e-05
|
| 292 |
- `warmup_steps`: 0.1
|
| 293 |
- `bf16`: True
|
| 294 |
+
- `eval_strategy`: steps
|
| 295 |
+
- `per_device_eval_batch_size`: 16
|
| 296 |
- `push_to_hub`: True
|
| 297 |
+
- `hub_model_id`: idqo/arcade-reranker
|
| 298 |
- `load_best_model_at_end`: True
|
| 299 |
|
| 300 |
#### All Hyperparameters
|
|
|
|
| 303 |
- `per_device_train_batch_size`: 16
|
| 304 |
- `num_train_epochs`: 3
|
| 305 |
- `max_steps`: -1
|
| 306 |
+
- `learning_rate`: 1e-05
|
| 307 |
- `lr_scheduler_type`: linear
|
| 308 |
- `lr_scheduler_kwargs`: None
|
| 309 |
- `warmup_steps`: 0.1
|
|
|
|
| 342 |
- `disable_tqdm`: False
|
| 343 |
- `project`: huggingface
|
| 344 |
- `trackio_space_id`: trackio
|
| 345 |
+
- `eval_strategy`: steps
|
| 346 |
+
- `per_device_eval_batch_size`: 16
|
| 347 |
- `prediction_loss_only`: True
|
| 348 |
- `eval_on_start`: False
|
| 349 |
- `eval_do_concat_batches`: True
|
|
|
|
| 356 |
- `enable_jit_checkpoint`: False
|
| 357 |
- `push_to_hub`: True
|
| 358 |
- `hub_private_repo`: None
|
| 359 |
+
- `hub_model_id`: idqo/arcade-reranker
|
| 360 |
- `hub_strategy`: every_save
|
| 361 |
- `hub_always_push`: False
|
| 362 |
- `hub_revision`: None
|
|
|
|
| 401 |
</details>
|
| 402 |
|
| 403 |
### Training Logs
|
| 404 |
+
| Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_R100_ndcg@10 | NanoNFCorpus_R100_ndcg@10 | NanoNQ_R100_ndcg@10 | NanoBEIR_R100_mean_ndcg@10 |
|
| 405 |
+
|:----------:|:-------:|:-------------:|:---------------:|:------------------------:|:-------------------------:|:--------------------:|:--------------------------:|
|
| 406 |
+
| 0.0070 | 1 | 0.9207 | - | - | - | - | - |
|
| 407 |
+
| 0.1748 | 25 | 0.7369 | 0.6563 | 0.6888 (+0.1484) | 0.4617 (+0.1367) | 0.7657 (+0.2651) | 0.6387 (+0.1834) |
|
| 408 |
+
| 0.3497 | 50 | 0.6174 | 0.5945 | 0.6764 (+0.1360) | 0.4403 (+0.1153) | 0.7658 (+0.2652) | 0.6275 (+0.1722) |
|
| 409 |
+
| 0.5245 | 75 | 0.5744 | 0.5895 | 0.6830 (+0.1426) | 0.4403 (+0.1152) | 0.7611 (+0.2605) | 0.6281 (+0.1728) |
|
| 410 |
+
| 0.6993 | 100 | 0.5693 | 0.5709 | 0.6668 (+0.1264) | 0.4510 (+0.1259) | 0.7652 (+0.2646) | 0.6277 (+0.1723) |
|
| 411 |
+
| 0.8741 | 125 | 0.5413 | 0.5636 | 0.6712 (+0.1308) | 0.4434 (+0.1184) | 0.7618 (+0.2611) | 0.6255 (+0.1701) |
|
| 412 |
+
| 1.0490 | 150 | 0.5437 | 0.5832 | 0.6706 (+0.1302) | 0.4441 (+0.1191) | 0.7574 (+0.2568) | 0.6240 (+0.1687) |
|
| 413 |
+
| 1.2238 | 175 | 0.5229 | 0.5676 | 0.6712 (+0.1308) | 0.4608 (+0.1358) | 0.7527 (+0.2521) | 0.6283 (+0.1729) |
|
| 414 |
+
| 1.3986 | 200 | 0.5015 | 0.5471 | 0.6712 (+0.1308) | 0.4611 (+0.1361) | 0.7527 (+0.2520) | 0.6283 (+0.1729) |
|
| 415 |
+
| 1.5734 | 225 | 0.4994 | 0.5501 | 0.6712 (+0.1308) | 0.4641 (+0.1390) | 0.7581 (+0.2575) | 0.6311 (+0.1757) |
|
| 416 |
+
| 1.7483 | 250 | 0.4999 | 0.5465 | 0.6707 (+0.1303) | 0.4570 (+0.1319) | 0.7544 (+0.2537) | 0.6274 (+0.1720) |
|
| 417 |
+
| 1.9231 | 275 | 0.4806 | 0.5441 | 0.6657 (+0.1253) | 0.4646 (+0.1396) | 0.7561 (+0.2555) | 0.6288 (+0.1734) |
|
| 418 |
+
| **2.0979** | **300** | **0.4568** | **0.5437** | **0.6572 (+0.1168)** | **0.4661 (+0.1411)** | **0.7508 (+0.2502)** | **0.6247 (+0.1694)** |
|
| 419 |
+
| 2.2727 | 325 | 0.4482 | 0.5479 | 0.6556 (+0.1152) | 0.4606 (+0.1355) | 0.7579 (+0.2573) | 0.6247 (+0.1693) |
|
| 420 |
+
| 2.4476 | 350 | 0.4549 | 0.5561 | 0.6560 (+0.1155) | 0.4643 (+0.1392) | 0.7494 (+0.2488) | 0.6232 (+0.1679) |
|
| 421 |
+
| 2.6224 | 375 | 0.4399 | 0.5529 | 0.6560 (+0.1155) | 0.4606 (+0.1355) | 0.7415 (+0.2409) | 0.6194 (+0.1640) |
|
| 422 |
|
| 423 |
* The bold row denotes the saved checkpoint.
|
| 424 |
|
model.safetensors
CHANGED
|
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|
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version https://git-lfs.github.com/spec/v1
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|
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size 598436708
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