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sobamchan
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st5-base-mean-100

Sentence Similarity
sentence-transformers
Safetensors
English
t5
feature-extraction
Generated from Trainer
dataset_size:557850
loss:MultipleNegativesRankingLoss
Inference Endpoints
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  1. 1_Pooling/config.json +10 -0
  2. README.md +403 -0
  3. config.json +61 -0
  4. config_sentence_transformers.json +10 -0
  5. model.safetensors +3 -0
  6. modules.json +20 -0
  7. optimizer.pt +3 -0
  8. rng_state.pth +3 -0
  9. scheduler.pt +3 -0
  10. sentence_bert_config.json +4 -0
  11. special_tokens_map.json +107 -0
  12. tokenizer.json +0 -0
  13. tokenizer_config.json +939 -0
  14. trainer_state.json +120 -0
  15. training_args.bin +3 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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+ ---
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+ language:
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+ - en
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+ tags:
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+ - sentence-transformers
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+ - sentence-similarity
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+ - feature-extraction
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+ - generated_from_trainer
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+ - dataset_size:557850
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+ - loss:MultipleNegativesRankingLoss
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+ base_model: google-t5/t5-base
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+ widget:
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+ - source_sentence: A man is jumping unto his filthy bed.
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+ sentences:
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+ - A young male is looking at a newspaper while 2 females walks past him.
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+ - The bed is dirty.
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+ - The man is on the moon.
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+ - source_sentence: A carefully balanced male stands on one foot near a clean ocean
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+ beach area.
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+ sentences:
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+ - A man is ouside near the beach.
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+ - Three policemen patrol the streets on bikes
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+ - A man is sitting on his couch.
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+ - source_sentence: The man is wearing a blue shirt.
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+ sentences:
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+ - Near the trashcan the man stood and smoked
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+ - A man in a blue shirt leans on a wall beside a road with a blue van and red car
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+ with water in the background.
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+ - A man in a black shirt is playing a guitar.
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+ - source_sentence: The girls are outdoors.
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+ sentences:
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+ - Two girls riding on an amusement part ride.
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+ - a guy laughs while doing laundry
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+ - Three girls are standing together in a room, one is listening, one is writing
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+ on a wall and the third is talking to them.
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+ - source_sentence: A construction worker peeking out of a manhole while his coworker
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+ sits on the sidewalk smiling.
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+ sentences:
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+ - A worker is looking out of a manhole.
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+ - A man is giving a presentation.
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+ - The workers are both inside the manhole.
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+ datasets:
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+ - sentence-transformers/all-nli
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+ pipeline_tag: sentence-similarity
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+ library_name: sentence-transformers
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+ ---
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+
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+ # SentenceTransformer based on google-t5/t5-base
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. 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.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** Sentence Transformer
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+ - **Base model:** [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) <!-- at revision a9723ea7f1b39c1eae772870f3b547bf6ef7e6c1 -->
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+ - **Maximum Sequence Length:** 256 tokens
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+ - **Output Dimensionality:** 768 dimensions
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+ - **Similarity Function:** Cosine Similarity
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+ - **Training Dataset:**
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+ - [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
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+ - **Language:** en
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
67
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
68
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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+
71
+ ### Full Model Architecture
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+
73
+ ```
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+ SentenceTransformer(
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+ (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: T5EncoderModel
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+ (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})
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+ (2): Normalize()
78
+ )
79
+ ```
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+
81
+ ## Usage
82
+
83
+ ### Direct Usage (Sentence Transformers)
84
+
85
+ First install the Sentence Transformers library:
86
+
87
+ ```bash
88
+ pip install -U sentence-transformers
89
+ ```
90
+
91
+ Then you can load this model and run inference.
92
+ ```python
93
+ from sentence_transformers import SentenceTransformer
94
+
95
+ # Download from the 🤗 Hub
96
+ model = SentenceTransformer("sentence_transformers_model_id")
97
+ # Run inference
98
+ sentences = [
99
+ 'A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.',
100
+ 'A worker is looking out of a manhole.',
101
+ 'The workers are both inside the manhole.',
102
+ ]
103
+ embeddings = model.encode(sentences)
104
+ print(embeddings.shape)
105
+ # [3, 768]
106
+
107
+ # Get the similarity scores for the embeddings
108
+ similarities = model.similarity(embeddings, embeddings)
109
+ print(similarities.shape)
110
+ # [3, 3]
111
+ ```
112
+
113
+ <!--
114
+ ### Direct Usage (Transformers)
115
+
116
+ <details><summary>Click to see the direct usage in Transformers</summary>
117
+
118
+ </details>
119
+ -->
120
+
121
+ <!--
122
+ ### Downstream Usage (Sentence Transformers)
123
+
124
+ You can finetune this model on your own dataset.
125
+
126
+ <details><summary>Click to expand</summary>
127
+
128
+ </details>
129
+ -->
130
+
131
+ <!--
132
+ ### Out-of-Scope Use
133
+
134
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
135
+ -->
136
+
137
+ <!--
138
+ ## Bias, Risks and Limitations
139
+
140
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
141
+ -->
142
+
143
+ <!--
144
+ ### Recommendations
145
+
146
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
147
+ -->
148
+
149
+ ## Training Details
150
+
151
+ ### Training Dataset
152
+
153
+ #### all-nli
154
+
155
+ * Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
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+ * Size: 557,850 training samples
157
+ * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
158
+ * Approximate statistics based on the first 1000 samples:
159
+ | | anchor | positive | negative |
160
+ |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
161
+ | type | string | string | string |
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+ | details | <ul><li>min: 6 tokens</li><li>mean: 9.96 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.79 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.02 tokens</li><li>max: 57 tokens</li></ul> |
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+ * Samples:
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+ | anchor | positive | negative |
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+ |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
166
+ | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> |
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+ | <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> |
168
+ | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code> |
169
+ * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
170
+ ```json
171
+ {
172
+ "scale": 20.0,
173
+ "similarity_fct": "cos_sim"
174
+ }
175
+ ```
176
+
177
+ ### Evaluation Dataset
178
+
179
+ #### all-nli
180
+
181
+ * Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
182
+ * Size: 6,584 evaluation samples
183
+ * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
184
+ * Approximate statistics based on the first 1000 samples:
185
+ | | anchor | positive | negative |
186
+ |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
187
+ | type | string | string | string |
188
+ | details | <ul><li>min: 5 tokens</li><li>mean: 19.41 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.69 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.35 tokens</li><li>max: 30 tokens</li></ul> |
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+ * Samples:
190
+ | anchor | positive | negative |
191
+ |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
192
+ | <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>The men are fighting outside a deli.</code> |
193
+ | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code> |
194
+ | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>A man selling donuts to a customer.</code> | <code>A woman drinks her coffee in a small cafe.</code> |
195
+ * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
196
+ ```json
197
+ {
198
+ "scale": 20.0,
199
+ "similarity_fct": "cos_sim"
200
+ }
201
+ ```
202
+
203
+ ### Training Hyperparameters
204
+ #### Non-Default Hyperparameters
205
+
206
+ - `eval_strategy`: steps
207
+ - `per_device_train_batch_size`: 64
208
+ - `per_device_eval_batch_size`: 64
209
+ - `learning_rate`: 1e-05
210
+ - `warmup_ratio`: 0.1
211
+ - `batch_sampler`: no_duplicates
212
+
213
+ #### All Hyperparameters
214
+ <details><summary>Click to expand</summary>
215
+
216
+ - `overwrite_output_dir`: False
217
+ - `do_predict`: False
218
+ - `eval_strategy`: steps
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+ - `prediction_loss_only`: True
220
+ - `per_device_train_batch_size`: 64
221
+ - `per_device_eval_batch_size`: 64
222
+ - `per_gpu_train_batch_size`: None
223
+ - `per_gpu_eval_batch_size`: None
224
+ - `gradient_accumulation_steps`: 1
225
+ - `eval_accumulation_steps`: None
226
+ - `torch_empty_cache_steps`: None
227
+ - `learning_rate`: 1e-05
228
+ - `weight_decay`: 0.0
229
+ - `adam_beta1`: 0.9
230
+ - `adam_beta2`: 0.999
231
+ - `adam_epsilon`: 1e-08
232
+ - `max_grad_norm`: 1.0
233
+ - `num_train_epochs`: 3
234
+ - `max_steps`: -1
235
+ - `lr_scheduler_type`: linear
236
+ - `lr_scheduler_kwargs`: {}
237
+ - `warmup_ratio`: 0.1
238
+ - `warmup_steps`: 0
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+ - `log_level`: passive
240
+ - `log_level_replica`: warning
241
+ - `log_on_each_node`: True
242
+ - `logging_nan_inf_filter`: True
243
+ - `save_safetensors`: True
244
+ - `save_on_each_node`: False
245
+ - `save_only_model`: False
246
+ - `restore_callback_states_from_checkpoint`: False
247
+ - `no_cuda`: False
248
+ - `use_cpu`: False
249
+ - `use_mps_device`: False
250
+ - `seed`: 42
251
+ - `data_seed`: None
252
+ - `jit_mode_eval`: False
253
+ - `use_ipex`: False
254
+ - `bf16`: False
255
+ - `fp16`: False
256
+ - `fp16_opt_level`: O1
257
+ - `half_precision_backend`: auto
258
+ - `bf16_full_eval`: False
259
+ - `fp16_full_eval`: False
260
+ - `tf32`: None
261
+ - `local_rank`: 0
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+ - `ddp_backend`: None
263
+ - `tpu_num_cores`: None
264
+ - `tpu_metrics_debug`: False
265
+ - `debug`: []
266
+ - `dataloader_drop_last`: False
267
+ - `dataloader_num_workers`: 0
268
+ - `dataloader_prefetch_factor`: None
269
+ - `past_index`: -1
270
+ - `disable_tqdm`: False
271
+ - `remove_unused_columns`: True
272
+ - `label_names`: None
273
+ - `load_best_model_at_end`: False
274
+ - `ignore_data_skip`: False
275
+ - `fsdp`: []
276
+ - `fsdp_min_num_params`: 0
277
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
278
+ - `fsdp_transformer_layer_cls_to_wrap`: None
279
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
280
+ - `deepspeed`: None
281
+ - `label_smoothing_factor`: 0.0
282
+ - `optim`: adamw_torch
283
+ - `optim_args`: None
284
+ - `adafactor`: False
285
+ - `group_by_length`: False
286
+ - `length_column_name`: length
287
+ - `ddp_find_unused_parameters`: None
288
+ - `ddp_bucket_cap_mb`: None
289
+ - `ddp_broadcast_buffers`: False
290
+ - `dataloader_pin_memory`: True
291
+ - `dataloader_persistent_workers`: False
292
+ - `skip_memory_metrics`: True
293
+ - `use_legacy_prediction_loop`: False
294
+ - `push_to_hub`: False
295
+ - `resume_from_checkpoint`: None
296
+ - `hub_model_id`: None
297
+ - `hub_strategy`: every_save
298
+ - `hub_private_repo`: None
299
+ - `hub_always_push`: False
300
+ - `gradient_checkpointing`: False
301
+ - `gradient_checkpointing_kwargs`: None
302
+ - `include_inputs_for_metrics`: False
303
+ - `include_for_metrics`: []
304
+ - `eval_do_concat_batches`: True
305
+ - `fp16_backend`: auto
306
+ - `push_to_hub_model_id`: None
307
+ - `push_to_hub_organization`: None
308
+ - `mp_parameters`:
309
+ - `auto_find_batch_size`: False
310
+ - `full_determinism`: False
311
+ - `torchdynamo`: None
312
+ - `ray_scope`: last
313
+ - `ddp_timeout`: 1800
314
+ - `torch_compile`: False
315
+ - `torch_compile_backend`: None
316
+ - `torch_compile_mode`: None
317
+ - `dispatch_batches`: None
318
+ - `split_batches`: None
319
+ - `include_tokens_per_second`: False
320
+ - `include_num_input_tokens_seen`: False
321
+ - `neftune_noise_alpha`: None
322
+ - `optim_target_modules`: None
323
+ - `batch_eval_metrics`: False
324
+ - `eval_on_start`: False
325
+ - `use_liger_kernel`: False
326
+ - `eval_use_gather_object`: False
327
+ - `average_tokens_across_devices`: False
328
+ - `prompts`: None
329
+ - `batch_sampler`: no_duplicates
330
+ - `multi_dataset_batch_sampler`: proportional
331
+
332
+ </details>
333
+
334
+ ### Training Logs
335
+ | Epoch | Step | Training Loss | Validation Loss |
336
+ |:------:|:----:|:-------------:|:---------------:|
337
+ | 0.0011 | 10 | - | 1.8733 |
338
+ | 0.0023 | 20 | - | 1.8726 |
339
+ | 0.0034 | 30 | - | 1.8714 |
340
+ | 0.0046 | 40 | - | 1.8697 |
341
+ | 0.0057 | 50 | - | 1.8675 |
342
+ | 0.0069 | 60 | - | 1.8649 |
343
+ | 0.0080 | 70 | - | 1.8619 |
344
+ | 0.0092 | 80 | - | 1.8584 |
345
+ | 0.0103 | 90 | - | 1.8544 |
346
+ | 0.0115 | 100 | 3.1046 | 1.8499 |
347
+
348
+
349
+ ### Framework Versions
350
+ - Python: 3.12.8
351
+ - Sentence Transformers: 3.4.1
352
+ - Transformers: 4.49.0
353
+ - PyTorch: 2.2.0+cu121
354
+ - Accelerate: 1.4.0
355
+ - Datasets: 3.3.2
356
+ - Tokenizers: 0.21.0
357
+
358
+ ## Citation
359
+
360
+ ### BibTeX
361
+
362
+ #### Sentence Transformers
363
+ ```bibtex
364
+ @inproceedings{reimers-2019-sentence-bert,
365
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
366
+ author = "Reimers, Nils and Gurevych, Iryna",
367
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
368
+ month = "11",
369
+ year = "2019",
370
+ publisher = "Association for Computational Linguistics",
371
+ url = "https://arxiv.org/abs/1908.10084",
372
+ }
373
+ ```
374
+
375
+ #### MultipleNegativesRankingLoss
376
+ ```bibtex
377
+ @misc{henderson2017efficient,
378
+ title={Efficient Natural Language Response Suggestion for Smart Reply},
379
+ 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},
380
+ year={2017},
381
+ eprint={1705.00652},
382
+ archivePrefix={arXiv},
383
+ primaryClass={cs.CL}
384
+ }
385
+ ```
386
+
387
+ <!--
388
+ ## Glossary
389
+
390
+ *Clearly define terms in order to be accessible across audiences.*
391
+ -->
392
+
393
+ <!--
394
+ ## Model Card Authors
395
+
396
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
397
+ -->
398
+
399
+ <!--
400
+ ## Model Card Contact
401
+
402
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
config.json ADDED
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1
+ {
2
+ "_name_or_path": "google-t5/t5-base",
3
+ "architectures": [
4
+ "T5EncoderModel"
5
+ ],
6
+ "classifier_dropout": 0.0,
7
+ "d_ff": 3072,
8
+ "d_kv": 64,
9
+ "d_model": 768,
10
+ "decoder_start_token_id": 0,
11
+ "dense_act_fn": "relu",
12
+ "dropout_rate": 0.1,
13
+ "eos_token_id": 1,
14
+ "feed_forward_proj": "relu",
15
+ "initializer_factor": 1.0,
16
+ "is_encoder_decoder": true,
17
+ "is_gated_act": false,
18
+ "layer_norm_epsilon": 1e-06,
19
+ "model_type": "t5",
20
+ "n_positions": 512,
21
+ "num_decoder_layers": 12,
22
+ "num_heads": 12,
23
+ "num_layers": 12,
24
+ "output_past": true,
25
+ "pad_token_id": 0,
26
+ "relative_attention_max_distance": 128,
27
+ "relative_attention_num_buckets": 32,
28
+ "task_specific_params": {
29
+ "summarization": {
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+ "early_stopping": true,
31
+ "length_penalty": 2.0,
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