Upload model
Browse files- README.md +148 -0
- config.json +44 -0
- decide.json +654 -0
- eval.json +416 -0
- model.safetensors +3 -0
- tokenizer.json +0 -0
README.md
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---
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license: apache-2.0
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language: en
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base_model: answerdotai/ModernBERT-base
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library_name: decide
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pipeline_tag: zero-shot-classification
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tags:
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- modernbert
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- decision-model
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- zero-shot-classification
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- calibrated
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---
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# decide-0.1.0
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A small, fast, **non-autoregressive decision model**. Given some state, an instruction and a set of candidate
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options, it scores every option in a single forward pass and returns calibrated probabilities. It answers
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three kinds of question with the same weights:
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- **choice**: a distribution over K mutually exclusive options
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- **noul**: the probability that a condition holds (a yes/no question)
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- **score**: an expected level on an ordered scale
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It is fine-tuned from [ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) (149M parameters).
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The packed input is `<instructions> <state> [SEP] [MASK] option0 [MASK] option1 ...`; a small MLP head scores the
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encoder state at each `[MASK]`. Each option attends only to the shared prefix and to itself and its position ids restart
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after the prefix, so an option's score depends on *(premise, that option)* alone: **permuting the options permutes the
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scores and changes nothing else** (measured max logit change under permutation: 1.4e-06).
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The model, tokenizer, training loop, evaluation and this card were all produced with the pure-Go toolchain in
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[jmwri/decide](https://github.com/jmwri/decide); no Python or PyTorch was used to train it.
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## Usage
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```go
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import "github.com/jmwri/decide"
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decide.SetDefault(decide.NewLocal("/path/to/this/model")) // or: decide pull --repo jmwri/decide
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ans, _ := decide.Decide(ctx, "Replication lag exceeded 45 seconds.", decide.Options{
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| 40 |
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{Key: "infrastructure", Description: "Database, hardware, network or server failures"},
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{Key: "billing", Description: "Invoices, payments, refunds"},
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}, "Classify the root cause domain.")
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```
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## Evaluation
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| 46 |
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Accuracy is top-option accuracy. ECE is the expected calibration error (15 bins) of the top-option confidence,
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after the per-kind temperature scaling shipped in `decide.json`.
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### Held-out validation splits of the training tasks
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| 51 |
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| task | n | accuracy | NLL | ECE |
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| 53 |
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| :-- | --: | --: | --: | --: |
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| 54 |
+
| **all** | 6307 | **78.7%** | 0.512 | 0.014 |
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| 55 |
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| kind: choice | 3782 | 77.5% | 0.549 | 0.016 |
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| kind: noul | 2146 | 84.6% | 0.352 | 0.012 |
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| kind: score | 379 | 57.0% | 1.034 | 0.068 |
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| ag_news | 300 | 91.7% | 0.257 | 0.047 |
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| amazon_polarity | 300 | 94.7% | 0.172 | 0.027 |
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| 60 |
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| arc | 200 | 38.5% | 1.331 | 0.101 |
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| arc_easy | 200 | 62.0% | 0.926 | 0.150 |
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| 62 |
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| banking77 | 300 | 96.3% | 0.106 | 0.022 |
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| 63 |
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| bitext_support | 300 | 99.7% | 0.015 | 0.011 |
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| 64 |
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| boolq | 300 | 75.7% | 0.493 | 0.050 |
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| 65 |
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| civil_comments | 156 | 73.1% (MAE 0.47 levels) | 0.606 | 0.065 |
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| 66 |
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| clinc150 | 300 | 98.0% | 0.059 | 0.019 |
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| 67 |
+
| cola | 200 | 76.5% | 0.524 | 0.055 |
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| 68 |
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| commonsense_qa | 300 | 62.3% | 0.891 | 0.092 |
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| 69 |
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| dbpedia14 | 300 | 73.3% | 0.918 | 0.162 |
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| go_emotions | 256 | 83.2% | 0.463 | 0.059 |
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| hellaswag | 300 | 62.3% | 0.927 | 0.069 |
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| mnli | 300 | 85.3% | 0.416 | 0.084 |
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| mrpc | 200 | 84.0% | 0.367 | 0.078 |
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| qnli | 200 | 90.5% | 0.234 | 0.057 |
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| qqp | 200 | 83.5% | 0.410 | 0.051 |
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+
| rte | 200 | 79.0% | 0.436 | 0.050 |
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| snli | 295 | 85.4% | 0.369 | 0.045 |
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| sst2 | 300 | 93.0% | 0.193 | 0.027 |
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| sst5 | 300 | 57.0% (MAE 0.56 levels) | 1.068 | 0.059 |
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| wanli | 300 | 73.0% | 0.591 | 0.050 |
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| winogrande | 300 | 56.3% | 0.757 | 0.134 |
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Tasks in this second table were **never trained on** (zero-shot to the model):
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### Held-out tasks
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| task | n | accuracy | NLL | ECE |
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| :-- | --: | --: | --: | --: |
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| **all** | 4693 | **47.9%** | 1.242 | 0.094 |
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| kind: choice | 2500 | 40.7% | 1.414 | 0.121 |
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| kind: noul | 1193 | 82.6% | 0.530 | 0.085 |
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| kind: score | 1000 | 24.4% | 1.662 | 0.108 |
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| emotion | 1000 | 51.6% | 1.454 | 0.168 |
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| mmlu | 1500 | 33.5% | 1.387 | 0.089 |
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| sms_spam | 1193 | 82.6% | 0.530 | 0.085 |
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| stsb | 1000 | 24.4% (MAE 0.93 levels) | 1.662 | 0.108 |
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## Training data
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Fine-tuned on 208762 examples built from the public datasets below (options are re-sampled and verbalised in several
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styles; classification tasks are also posed as yes/no verification questions). Licenses are as declared on the
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Hugging Face hub; **review them before redistributing or commercialising the weights**, since several datasets
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carry share-alike or unspecified terms.
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| dataset | train examples | license (as declared) |
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| :-- | --: | :-- |
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| ag_news | 6000 | unknown |
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| amazon_polarity | 8000 | apache-2.0 |
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| 109 |
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| arc | 1119 | cc-by-sa-4.0 |
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| arc_easy | 2251 | cc-by-sa-4.0 |
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| 111 |
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| banking77 | 9993 | cc-by-4.0 |
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| 112 |
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| bitext_support | 8990 | cdla-sharing-1.0 |
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| 113 |
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| boolq | 9000 | cc-by-sa-3.0 |
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| 114 |
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| civil_comments | 9391 | cc0-1.0 |
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| 115 |
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| clinc150 | 12000 | cc-by-3.0 |
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| 116 |
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| cola | 6000 | other (GLUE) |
|
| 117 |
+
| commonsense_qa | 9000 | mit |
|
| 118 |
+
| dbpedia14 | 8000 | cc-by-sa-3.0 |
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| 119 |
+
| emotion (held out, evaluation only) | 0 | other (dair-ai) |
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| 120 |
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| go_emotions | 7520 | apache-2.0 |
|
| 121 |
+
| hellaswag | 9000 | mit |
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| 122 |
+
| mmlu (held out, evaluation only) | 0 | mit |
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| 123 |
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| mnli | 24000 | cc-by-3.0/cc-by-sa-3.0/mit/other |
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| 124 |
+
| mrpc | 3668 | other (GLUE) |
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| 125 |
+
| qnli | 8000 | other (GLUE) |
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| 126 |
+
| qqp | 8000 | other (GLUE) |
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| 127 |
+
| rte | 2490 | other (GLUE) |
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| 128 |
+
| sms_spam (held out, evaluation only) | 0 | unknown (UCI) |
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| 129 |
+
| snli | 15976 | cc-by-sa-4.0 |
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| 130 |
+
| sst2 | 8000 | other (GLUE) |
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| 131 |
+
| sst5 | 8500 | unspecified (SetFit/sst5) |
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| 132 |
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| stsb (held out, evaluation only) | 0 | other (GLUE) |
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| 133 |
+
| wanli | 16000 | cc-by-4.0 |
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| 134 |
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| winogrande | 8000 | cc-by-4.0 |
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| 135 |
+
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| 136 |
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## Limitations
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| 137 |
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| 138 |
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- English only. Inputs are limited to 8192 tokens; option sets are best kept under ~10 (use a two-stage taxonomy beyond that).
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- Trained on public classification, inference and multiple-choice datasets, so it is strongest on those styles of
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| 140 |
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question. Explicit, descriptive option texts work better than bare labels. Domain-specific jargon or rubrics with
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| 141 |
+
no descriptive criteria fall back to general language priors.
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| 142 |
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- Probabilities are calibrated on held-out data from the training tasks; on very different distributions they will
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| 143 |
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be over-confident. Use a confidence gate and route uncertain cases to review.
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| 144 |
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- Not for decisions that affect people's rights, safety or livelihood without human oversight.
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| 145 |
+
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| 146 |
+
## License
|
| 147 |
+
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| 148 |
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Weights: Apache-2.0 (ModernBERT-base is Apache-2.0), subject to the training-data terms above.
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config.json
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| 1 |
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{
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| 2 |
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"_name_or_path": "ModernBERT-base",
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| 3 |
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"architectures": [
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| 4 |
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"ModernBertForMaskedLM"
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| 5 |
+
],
|
| 6 |
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"attention_bias": false,
|
| 7 |
+
"attention_dropout": 0.0,
|
| 8 |
+
"bos_token_id": 50281,
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| 9 |
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"classifier_activation": "gelu",
|
| 10 |
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"classifier_bias": false,
|
| 11 |
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"classifier_dropout": 0.0,
|
| 12 |
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"classifier_pooling": "mean",
|
| 13 |
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"cls_token_id": 50281,
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| 14 |
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"decoder_bias": true,
|
| 15 |
+
"deterministic_flash_attn": false,
|
| 16 |
+
"embedding_dropout": 0.0,
|
| 17 |
+
"eos_token_id": 50282,
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| 18 |
+
"global_attn_every_n_layers": 3,
|
| 19 |
+
"global_rope_theta": 160000.0,
|
| 20 |
+
"gradient_checkpointing": false,
|
| 21 |
+
"hidden_activation": "gelu",
|
| 22 |
+
"hidden_size": 768,
|
| 23 |
+
"initializer_cutoff_factor": 2.0,
|
| 24 |
+
"initializer_range": 0.02,
|
| 25 |
+
"intermediate_size": 1152,
|
| 26 |
+
"layer_norm_eps": 1e-05,
|
| 27 |
+
"local_attention": 128,
|
| 28 |
+
"local_rope_theta": 10000.0,
|
| 29 |
+
"max_position_embeddings": 8192,
|
| 30 |
+
"mlp_bias": false,
|
| 31 |
+
"mlp_dropout": 0.0,
|
| 32 |
+
"model_type": "modernbert",
|
| 33 |
+
"norm_bias": false,
|
| 34 |
+
"norm_eps": 1e-05,
|
| 35 |
+
"num_attention_heads": 12,
|
| 36 |
+
"num_hidden_layers": 22,
|
| 37 |
+
"pad_token_id": 50283,
|
| 38 |
+
"position_embedding_type": "absolute",
|
| 39 |
+
"sep_token_id": 50282,
|
| 40 |
+
"tie_word_embeddings": true,
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| 41 |
+
"torch_dtype": "float32",
|
| 42 |
+
"transformers_version": "4.47.0.dev0",
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| 43 |
+
"vocab_size": 50368
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| 44 |
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}
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decide.json
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|
eval.json
ADDED
|
@@ -0,0 +1,416 @@
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|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:7635aef6ea1d4119675e0835a480e2bdfad19edfab7159eecad51ee7a216db53
|
| 3 |
+
size 597263604
|
tokenizer.json
ADDED
|
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